#Loading required libraries
library(phyloseq)
# Define packages
## CRAN repositories
cran_packages <- c("bookdown", "knitr", "tidyverse", "plyr", "grid", "gridExtra", "kableExtra", "xtable", "ggpubr")
## Bioconductor repository
bioc_packages <- c("phyloseq", "dada2", "DECIPHER", "phangorn", "ggpubr", "BiocManager","DESeq2", "microbiome", "philr")
## GitHub repository
git_source <- c("twbattaglia/btools", "gmteunisse/Fantaxtic", "MadsAlbertsen/ampvis2", "opisthokonta/tsnemicrobiota") # fuente/nombre del paquete
git_packages <- c("btools", "fantaxtic", "ampvis2", "tsnemicrobiota") # nombre del paquete
# Install CRAN packages
.inst <- cran_packages %in% installed.packages()
if(any(!.inst)) {
install.packages(cran_packages[!.inst])
}
# Install Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
.inst <- bioc_packages %in% installed.packages()
if(any(!.inst)) {
BiocManager::install(bioc_packages[!.inst])
}
## Bioconductor version 3.12 (BiocManager 1.30.16), R 4.0.3 (2020-10-10)
## Installing package(s) 'philr'
## also installing the dependency 'ggtree'
## Warning in .inet_warning(msg): installation of package 'ggtree' had non-zero
## exit status
## Warning in .inet_warning(msg): installation of package 'philr' had non-zero exit
## status
## Updating HTML index of packages in '.Library'
## Making 'packages.html' ... done
## Old packages: 'aplot', 'class', 'fansi', 'fftwtools', 'foreign', 'MASS',
## 'nlme', 'nnet', 'Rcpp', 'reticulate', 'spatial', 'tidytree'
# Install GitHub packages
.inst <- git_source %in% installed.packages()
if(any(!.inst)) {
devtools::install_github(git_source[!.inst])
}
## Skipping install of 'btools' from a github remote, the SHA1 (fa21d4ca) has not changed since last install.
## Use `force = TRUE` to force installation
## Skipping install of 'fantaxtic' from a github remote, the SHA1 (3342beee) has not changed since last install.
## Use `force = TRUE` to force installation
## Skipping install of 'ampvis2' from a github remote, the SHA1 (f48b708f) has not changed since last install.
## Use `force = TRUE` to force installation
## Skipping install of 'tsnemicrobiota' from a github remote, the SHA1 (cffbab72) has not changed since last install.
## Use `force = TRUE` to force installation
# Loading required packages
sapply(c(cran_packages, bioc_packages, git_packages), require, character.only = TRUE)
## Loading required package: bookdown
## Loading required package: knitr
## Loading required package: tidyverse
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.1 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## Loading required package: plyr
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
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## Attaching package: 'plyr'
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## is available with R version '4.1'; see https://bioconductor.org/install
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## Welcome to Bioconductor
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## Loading required package: tsnemicrobiota
## bookdown knitr tidyverse plyr grid
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## TRUE TRUE TRUE TRUE TRUE
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#Extract sample names
miseq_path <- "./" # CHANGE to the directory containing the fastq files after unzipping.
# Sort ensures forward/reverse reads are in same order
fnFs <- sort(list.files(miseq_path, pattern="_R1_001.fastq"))
# Extract sample names, assuming filenames have format: SAMPLENAME_XXX.fastq
sampleNames <- sapply(strsplit(fnFs, "_"), `[`, 1)
# Specify the full path to the fnFs
fnFs <- file.path(miseq_path, fnFs)
fnFs[1:3]
## [1] ".//CUN02-V2_1_L001_R1_001.fastq" ".//CUN02-V3_4_L001_R1_001.fastq"
## [3] ".//CUN03-V2_7_L001_R1_001.fastq"
#plot quality profile two samples
plotQualityProfile(fnFs[1:2])
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
#plot quality profile
plotQualityProfile(fnFs[c(1,11)])
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
#names(filtFs) <- sample.names
sample.names <- sapply(strsplit(basename(fnFs), "_"), `[`, 1)
head(sample.names)
## [1] "CUN02-V2" "CUN02-V3" "CUN03-V2" "CUN03-V3" "HCB01-V2" "HCB01-V3"
filt_path <- file.path(miseq_path, "filtered") # Place filtered files in filtered/ subdirectory
if(!file_test("-d", filt_path)) dir.create(filt_path)
filtFs <- file.path(filt_path, paste0(sampleNames, "_F_filt.fastq.gz"))
#Fiter and trim
outF1s<- filterAndTrim(fnFs, filtFs, truncLen=288, maxN=0, maxEE=2, truncQ=2, rm.phix=FALSE, compress=FALSE, verbose=TRUE)
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/CUN02-V2_F_filt.fastq.gz
## Read in 77582, output 55882 (72%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/CUN02-V3_F_filt.fastq.gz
## Read in 78500, output 56171 (71.6%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/CUN03-V2_F_filt.fastq.gz
## Read in 73044, output 50919 (69.7%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/CUN03-V3_F_filt.fastq.gz
## Read in 85148, output 60267 (70.8%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB01-V2_F_filt.fastq.gz
## Read in 77942, output 54420 (69.8%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB01-V3_F_filt.fastq.gz
## Read in 76092, output 51823 (68.1%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB02-V2_F_filt.fastq.gz
## Read in 62110, output 43706 (70.4%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB02-V3_F_filt.fastq.gz
## Read in 79907, output 56642 (70.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB03-V2_F_filt.fastq.gz
## Read in 90718, output 59814 (65.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB03-V3_F_filt.fastq.gz
## Read in 74075, output 49537 (66.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB04-V2_F_filt.fastq.gz
## Read in 78515, output 54934 (70%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB04-V3_F_filt.fastq.gz
## Read in 87173, output 60951 (69.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB06-V2_F_filt.fastq.gz
## Read in 86527, output 60214 (69.6%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB08-V2_F_filt.fastq.gz
## Read in 73143, output 49951 (68.3%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB09-V2_F_filt.fastq.gz
## Read in 92119, output 61560 (66.8%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB10-V2_F_filt.fastq.gz
## Read in 50022, output 35458 (70.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB12-V2_F_filt.fastq.gz
## Read in 73294, output 50478 (68.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HCB13-V3_F_filt.fastq.gz
## Read in 80869, output 55595 (68.7%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA01-V2_F_filt.fastq.gz
## Read in 67690, output 46666 (68.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA01-V3_F_filt.fastq.gz
## Read in 81275, output 57916 (71.3%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA02-V2_F_filt.fastq.gz
## Read in 81425, output 55645 (68.3%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA02-V3_F_filt.fastq.gz
## Read in 77772, output 53549 (68.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA03-V2_F_filt.fastq.gz
## Read in 78250, output 53339 (68.2%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA03-V3_F_filt.fastq.gz
## Read in 86178, output 60153 (69.8%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA04-V2_F_filt.fastq.gz
## Read in 103598, output 71280 (68.8%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA04-V3_F_filt.fastq.gz
## Read in 74184, output 50179 (67.6%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVA05-V3_F_filt.fastq.gz
## Read in 94023, output 63596 (67.6%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVR01-V2_F_filt.fastq.gz
## Read in 48149, output 32090 (66.6%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/HVR02-V2_F_filt.fastq.gz
## Read in 66957, output 44071 (65.8%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/ISBS01-V2_F_filt.fastq.gz
## Read in 82565, output 57700 (69.9%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/ISBS03-V2_F_filt.fastq.gz
## Read in 244723, output 168013 (68.7%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/ISBS06-V2_F_filt.fastq.gz
## Read in 81278, output 55876 (68.7%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/ISBS08-V2_F_filt.fastq.gz
## Read in 44945, output 28090 (62.5%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/ISBS09-V2_F_filt.fastq.gz
## Read in 60124, output 38568 (64.1%) filtered sequences.
## Overwriting file:/home/eduvb/qiime2/Rstudio/filtered/ISBS10-V2_F_filt.fastq.gz
## Read in 72508, output 48676 (67.1%) filtered sequences.
head(outF1s)
## reads.in reads.out
## CUN02-V2_1_L001_R1_001.fastq 77582 55882
## CUN02-V3_4_L001_R1_001.fastq 78500 56171
## CUN03-V2_7_L001_R1_001.fastq 73044 50919
## CUN03-V3_9_L001_R1_001.fastq 85148 60267
## HCB01-V2_13_L001_R1_001.fastq 77942 54420
## HCB01-V3_16_L001_R1_001.fastq 76092 51823
#Dereplication
derepFs <- derepFastq(filtFs, verbose=TRUE)
## Dereplicating sequence entries in Fastq file: .//filtered/CUN02-V2_F_filt.fastq.gz
## Encountered 19350 unique sequences from 55882 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/CUN02-V3_F_filt.fastq.gz
## Encountered 20061 unique sequences from 56171 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/CUN03-V2_F_filt.fastq.gz
## Encountered 20630 unique sequences from 50919 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/CUN03-V3_F_filt.fastq.gz
## Encountered 24207 unique sequences from 60267 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB01-V2_F_filt.fastq.gz
## Encountered 22104 unique sequences from 54420 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB01-V3_F_filt.fastq.gz
## Encountered 19675 unique sequences from 51823 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB02-V2_F_filt.fastq.gz
## Encountered 14022 unique sequences from 43706 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB02-V3_F_filt.fastq.gz
## Encountered 18830 unique sequences from 56642 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB03-V2_F_filt.fastq.gz
## Encountered 20981 unique sequences from 59814 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB03-V3_F_filt.fastq.gz
## Encountered 17912 unique sequences from 49537 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB04-V2_F_filt.fastq.gz
## Encountered 17659 unique sequences from 54934 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB04-V3_F_filt.fastq.gz
## Encountered 19467 unique sequences from 60951 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB06-V2_F_filt.fastq.gz
## Encountered 23786 unique sequences from 60214 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB08-V2_F_filt.fastq.gz
## Encountered 17432 unique sequences from 49951 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB09-V2_F_filt.fastq.gz
## Encountered 24622 unique sequences from 61560 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB10-V2_F_filt.fastq.gz
## Encountered 12603 unique sequences from 35458 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB12-V2_F_filt.fastq.gz
## Encountered 17518 unique sequences from 50478 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HCB13-V3_F_filt.fastq.gz
## Encountered 22507 unique sequences from 55595 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA01-V2_F_filt.fastq.gz
## Encountered 18268 unique sequences from 46666 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA01-V3_F_filt.fastq.gz
## Encountered 21055 unique sequences from 57916 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA02-V2_F_filt.fastq.gz
## Encountered 21588 unique sequences from 55645 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA02-V3_F_filt.fastq.gz
## Encountered 21872 unique sequences from 53549 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA03-V2_F_filt.fastq.gz
## Encountered 18099 unique sequences from 53339 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA03-V3_F_filt.fastq.gz
## Encountered 17148 unique sequences from 60153 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA04-V2_F_filt.fastq.gz
## Encountered 31215 unique sequences from 71280 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA04-V3_F_filt.fastq.gz
## Encountered 20455 unique sequences from 50179 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVA05-V3_F_filt.fastq.gz
## Encountered 25355 unique sequences from 63596 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVR01-V2_F_filt.fastq.gz
## Encountered 13935 unique sequences from 32090 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/HVR02-V2_F_filt.fastq.gz
## Encountered 17785 unique sequences from 44071 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/ISBS01-V2_F_filt.fastq.gz
## Encountered 21785 unique sequences from 57700 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/ISBS03-V2_F_filt.fastq.gz
## Encountered 56826 unique sequences from 168013 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/ISBS06-V2_F_filt.fastq.gz
## Encountered 25194 unique sequences from 55876 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/ISBS08-V2_F_filt.fastq.gz
## Encountered 12163 unique sequences from 28090 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/ISBS09-V2_F_filt.fastq.gz
## Encountered 13497 unique sequences from 38568 total sequences read.
## Dereplicating sequence entries in Fastq file: .//filtered/ISBS10-V2_F_filt.fastq.gz
## Encountered 17999 unique sequences from 48676 total sequences read.
# Name the derep-class objects by the sample names
names(derepFs) <- sampleNames
#Learn errors
errF <- learnErrors(filtFs, multithread=TRUE)
## 107478144 total bases in 373188 reads from 7 samples will be used for learning the error rates.
plotErrors(errF)
#DADA2
dadaFs <- dada(derepFs, err=errF, multithread=TRUE)
## Sample 1 - 55882 reads in 19350 unique sequences.
## Sample 2 - 56171 reads in 20061 unique sequences.
## Sample 3 - 50919 reads in 20630 unique sequences.
## Sample 4 - 60267 reads in 24207 unique sequences.
## Sample 5 - 54420 reads in 22104 unique sequences.
## Sample 6 - 51823 reads in 19675 unique sequences.
## Sample 7 - 43706 reads in 14022 unique sequences.
## Sample 8 - 56642 reads in 18830 unique sequences.
## Sample 9 - 59814 reads in 20981 unique sequences.
## Sample 10 - 49537 reads in 17912 unique sequences.
## Sample 11 - 54934 reads in 17659 unique sequences.
## Sample 12 - 60951 reads in 19467 unique sequences.
## Sample 13 - 60214 reads in 23786 unique sequences.
## Sample 14 - 49951 reads in 17432 unique sequences.
## Sample 15 - 61560 reads in 24622 unique sequences.
## Sample 16 - 35458 reads in 12603 unique sequences.
## Sample 17 - 50478 reads in 17518 unique sequences.
## Sample 18 - 55595 reads in 22507 unique sequences.
## Sample 19 - 46666 reads in 18268 unique sequences.
## Sample 20 - 57916 reads in 21055 unique sequences.
## Sample 21 - 55645 reads in 21588 unique sequences.
## Sample 22 - 53549 reads in 21872 unique sequences.
## Sample 23 - 53339 reads in 18099 unique sequences.
## Sample 24 - 60153 reads in 17148 unique sequences.
## Sample 25 - 71280 reads in 31215 unique sequences.
## Sample 26 - 50179 reads in 20455 unique sequences.
## Sample 27 - 63596 reads in 25355 unique sequences.
## Sample 28 - 32090 reads in 13935 unique sequences.
## Sample 29 - 44071 reads in 17785 unique sequences.
## Sample 30 - 57700 reads in 21785 unique sequences.
## Sample 31 - 168013 reads in 56826 unique sequences.
## Sample 32 - 55876 reads in 25194 unique sequences.
## Sample 33 - 28090 reads in 12163 unique sequences.
## Sample 34 - 38568 reads in 13497 unique sequences.
## Sample 35 - 48676 reads in 17999 unique sequences.
#Inspect dada2 object
dadaFs[[1]]
## dada-class: object describing DADA2 denoising results
## 383 sequence variants were inferred from 19350 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
##Construct sequence table and remove chimeras
library(dada2)
library(Rcpp)
#BiocManager::install('taRifx')
library(taRifx)
##
## Attaching package: 'taRifx'
## The following object is masked from 'package:SummarizedExperiment':
##
## shift
## The following object is masked from 'package:GenomicRanges':
##
## shift
## The following object is masked from 'package:IRanges':
##
## shift
## The following object is masked from 'package:S4Vectors':
##
## first
## The following objects are masked from 'package:dplyr':
##
## between, distinct, first, last
## The following object is masked from 'package:purrr':
##
## rep_along
mergers <-merge.list(dadaFs, derepFs)
seqtabAll <- makeSequenceTable(mergers[!grepl("Mock", names(mergers))])
table(nchar(getSequences(seqtabAll)))
##
## 288
## 10484
#remove chimeric sequences
seqtabNoC <- removeBimeraDenovo(seqtabAll)
#Assign taxonomy <<
#SILVA Reference Genetic Database
#wget \
# -O "silva_nr99_v138.1_train_set.fa.gz" \
# "https://zenodo.org/record/4587955/files/silva_nr99_v138.1_train_set.fa.gz?download=1"
#wget \
# -O "silva_species_assignment_v138.1.fa.gz" \
# "https://zenodo.org/record/4587955/files/silva_species_assignment_v138.1.fa.gz?download=1"
tt <- assignTaxonomy(seqtabNoC, "silva_nr99_v138.1_train_set.fa.gz")
#tt.plus <- addSpecies(tt, "silva_species_assignment_v138.1.fa.gz", verbose=TRUE)
#load("tt.plus.Rda")
#unname(head(tt.plus))
unname(head(tt))
## [,1] [,2] [,3] [,4]
## [1,] "Bacteria" "Firmicutes" "Bacilli" "Lactobacillales"
## [2,] "Bacteria" "Firmicutes" "Bacilli" "Lactobacillales"
## [3,] "Bacteria" "Firmicutes" "Clostridia" "Lachnospirales"
## [4,] "Bacteria" "Firmicutes" "Bacilli" "Lactobacillales"
## [5,] "Bacteria" "Proteobacteria" "Gammaproteobacteria" "Enterobacterales"
## [6,] "Bacteria" "Proteobacteria" "Gammaproteobacteria" "Enterobacterales"
## [,5] [,6]
## [1,] "Streptococcaceae" "Streptococcus"
## [2,] "Streptococcaceae" "Streptococcus"
## [3,] "Lachnospiraceae" "Blautia"
## [4,] "Streptococcaceae" "Streptococcus"
## [5,] "Enterobacteriaceae" "Escherichia-Shigella"
## [6,] "Enterobacteriaceae" "Escherichia-Shigella"
taxTab <- tt
#Construct phylogenetic tree
#using the DECIPHER R package
seqs <- getSequences(seqtabNoC)
names(seqs) <- seqs # This propagates to the tip labels of the tree
alignment <- AlignSeqs(DNAStringSet(seqs), anchor=NA,verbose=FALSE)
#phangorn R package is then used to construct a phylogenetic tree
phangAlign <- phyDat(as(alignment, "matrix"), type="DNA")
dm <- dist.ml(phangAlign)
treeNJ <- NJ(dm) # Note, tip order != sequence order
fit = pml(treeNJ, data=phangAlign)
## negative edges length changed to 0!
fitGTR <- update(fit, k=4, inv=0.2)
fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
rearrangement = "stochastic", control = pml.control(trace = 0))
detach("package:phangorn", unload=TRUE)
##Combine data into a phyloseq object
#Load metadata file
samdf <- read.csv("Metadata_OTUs_mod_1.csv",header=TRUE)
rownames(samdf) <- samdf$SampleID
keep.cols <- c("SampleID", "Edad_a_la_inclusion", "Sexo", "Respuesta_100", "aloTPH_previo_si_no", "Citogenética_alto_riesgo", "Afect_Extramedular_PET_screening_si_no", "Dias_producción", "CRS_si_no", "Grado_máx_CRS", "HOSPITAL", "Visit")
samdf <- samdf[rownames(seqtabAll), keep.cols]
# the sample metadata, the sequence taxonomies, and the phylogenetic tree – can now be combined into a single object
ps <- phyloseq(otu_table(seqtabNoC, taxa_are_rows=FALSE),
sample_data(samdf),
tax_table(taxTab),phy_tree(fitGTR$tree))
ps
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 6149 taxa and 35 samples ]
## sample_data() Sample Data: [ 35 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 6149 taxa by 6 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 6149 tips and 6147 internal nodes ]
#Loading required libraries
library(tidyverse)
#BiocManager::install("curatedMetagenomicData")
library(curatedMetagenomicData)
## Loading required package: AnnotationHub
## Loading required package: BiocFileCache
## Loading required package: dbplyr
##
## Attaching package: 'dbplyr'
## The following objects are masked from 'package:dplyr':
##
## ident, sql
##
## Attaching package: 'AnnotationHub'
## The following object is masked from 'package:Biobase':
##
## cache
## Loading required package: ExperimentHub
## _ _
## ___ _ _ _ __ __ _| |_ ___ __| |
## / __| | | | '__/ _` | __/ _ \/ _` |
## | (__| |_| | | | (_| | || __/ (_| |
## \___|\__,_|_| \__,_|\__\___|\__,_|
## __ __ _ _
## | \/ | ___| |_ __ _ __ _ ___ _ __ ___ _ __ ___ (_) ___
## | |\/| |/ _ \ __/ _` |/ _` |/ _ \ '_ \ / _ \| '_ ` _ \| |/ __|
## | | | | __/ || (_| | (_| | __/ | | | (_) | | | | | | | (__
## |_| |_|\___|\__\__,_|\__, |\___|_| |_|\___/|_| |_| |_|_|\___|
## ____ _ |___/
## | _ \ __ _| |_ __ _
## | | | |/ _` | __/ _` |
## | |_| | (_| | || (_| |
## |____/ \__,_|\__\__,_|
library(phyloseq)
library(DESeq2)
#BiocManager::install("apeglm")
library(apeglm)
#BiocManager::install("scran")
#BiocManager::install("zinbwave")
library(zinbwave)
## Loading required package: SingleCellExperiment
library(scran)
library(cowplot)
##
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggpubr':
##
## get_legend
#BiocManager::install("VennDiagram")
library(VennDiagram)
## Loading required package: futile.logger
##
## Attaching package: 'VennDiagram'
## The following object is masked from 'package:ape':
##
## rotate
## The following object is masked from 'package:ggpubr':
##
## rotate
#BiocManager::install("pscl")
library(pscl)
## Classes and Methods for R developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University
## Simon Jackman
## hurdle and zeroinfl functions by Achim Zeileis
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
#####Phyloseq object#####
#Taxonomic Filtering
# Show available ranks in the dataset
rank_names(ps)
## [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus"
## Create table, number of features for each phyla
table(tax_table(ps)[, "Phylum"], exclude = NULL)
##
## Actinobacteriota Bacteroidota Campylobacterota Cyanobacteria
## 622 749 6 9
## Desulfobacterota Euryarchaeota Firmicutes Fusobacteriota
## 21 8 4290 12
## Patescibacteria Proteobacteria Synergistota Verrucomicrobiota
## 18 246 12 30
## <NA>
## 126
#features were annotated with a Phylum of NA. These features are probably artifacts in a dataset like this, and should be removed.
ps <- subset_taxa(ps, !is.na(Phylum) & !Phylum %in% c("", "uncharacterized"))
# Compute prevalence of each feature, store as data.frame
prevdf = apply(X = otu_table(ps),
MARGIN = ifelse(taxa_are_rows(ps), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
# Add taxonomy and total read counts to this data.frame
prevdf = data.frame(Prevalence = prevdf,
TotalAbundance = taxa_sums(ps),
tax_table(ps))
#Create a new phyloseq-object
psv2 <- ps
#Define levels as factor
sample_data(psv2)$Respuesta_100 <- as.factor(sample_data(psv2)$Respuesta_100)
levels(sample_data(psv2)$Respuesta_100)
## [1] "PR" "sCR" "VGPR"
#rename <<VGPR>> en <<sCR>>
levels(sample_data(psv2)$Respuesta_100)[3] <- "sCR"
levels(sample_data(psv2)$Respuesta_100)
## [1] "PR" "sCR"
sample_data(psv2)$Respuesta_100
## [1] sCR sCR sCR sCR PR PR PR PR sCR sCR sCR sCR sCR PR sCR sCR sCR sCR PR
## [20] PR sCR sCR sCR sCR sCR sCR PR sCR sCR sCR sCR PR sCR PR sCR
## Levels: PR sCR
#Create a new phyloseq-object filtered by <<Visit==V2>>
psv2 <- subset_samples(psv2, Visit=="V2" )
sample_data(psv2)
## SampleID Edad_a_la_inclusion Sexo Respuesta_100
## CUN02-V2 CUN02-V2 43 MUJER sCR
## CUN03-V2 CUN03-V2 45 HOMBRE sCR
## HCB01-V2 HCB01-V2 66 HOMBRE PR
## HCB02-V2 HCB02-V2 66 HOMBRE PR
## HCB03-V2 HCB03-V2 60 HOMBRE sCR
## HCB04-V2 HCB04-V2 55 MUJER sCR
## HCB06-V2 HCB06-V2 73 MUJER sCR
## HCB08-V2 HCB08-V2 62 MUJER PR
## HCB09-V2 HCB09-V2 53 HOMBRE sCR
## HCB10-V2 HCB10-V2 52 HOMBRE sCR
## HCB12-V2 HCB12-V2 64 HOMBRE sCR
## HVA01-V2 HVA01-V2 58 HOMBRE PR
## HVA02-V2 HVA02-V2 67 HOMBRE sCR
## HVA03-V2 HVA03-V2 61 HOMBRE sCR
## HVA04-V2 HVA04-V2 74 MUJER sCR
## HVR01-V2 HVR01-V2 65 HOMBRE sCR
## HVR02-V2 HVR02-V2 46 HOMBRE sCR
## ISBS01-V2 ISBS01-V2 56 HOMBRE sCR
## ISBS03-V2 ISBS03-V2 70 MUJER sCR
## ISBS06-V2 ISBS06-V2 60 HOMBRE PR
## ISBS08-V2 ISBS08-V2 53 HOMBRE sCR
## ISBS09-V2 ISBS09-V2 65 MUJER PR
## ISBS10-V2 ISBS10-V2 68 HOMBRE sCR
## aloTPH_previo_si_no Citogenética_alto_riesgo
## CUN02-V2 NO NO
## CUN03-V2 NO SI
## HCB01-V2 NO NO
## HCB02-V2 NO SI
## HCB03-V2 NO SI
## HCB04-V2 NO NO
## HCB06-V2 NO NO
## HCB08-V2 NO NO
## HCB09-V2 NO NO
## HCB10-V2 SI NO
## HCB12-V2 NO SI
## HVA01-V2 NO NO
## HVA02-V2 NO NO
## HVA03-V2 SI NO
## HVA04-V2 NO NO
## HVR01-V2 NO
## HVR02-V2 NO
## ISBS01-V2 NO SI
## ISBS03-V2 NO
## ISBS06-V2 NO NO
## ISBS08-V2 NO
## ISBS09-V2 NO NO
## ISBS10-V2 NO SI
## Afect_Extramedular_PET_screening_si_no Dias_producción CRS_si_no
## CUN02-V2 SI 120 SI
## CUN03-V2 NO 100 SI
## HCB01-V2 SI 110 SI
## HCB02-V2 NO 110 SI
## HCB03-V2 SI 110 SI
## HCB04-V2 NO 110 SI
## HCB06-V2 NO 90 SI
## HCB08-V2 SI 100 SI
## HCB09-V2 NO 90 SI
## HCB10-V2 SI 100 SI
## HCB12-V2 NO 110 NO
## HVA01-V2 NO 120 SI
## HVA02-V2 SI 100 SI
## HVA03-V2 SI 140 SI
## HVA04-V2 NO 100 SI
## HVR01-V2 SI 110 SI
## HVR02-V2 SI 120 SI
## ISBS01-V2 SI 120 SI
## ISBS03-V2 SI 100 SI
## ISBS06-V2 NO 130 SI
## ISBS08-V2 SI 110 SI
## ISBS09-V2 SI 100 SI
## ISBS10-V2 SI 140 NO
## Grado_máx_CRS HOSPITAL Visit
## CUN02-V2 10 CUN V2
## CUN03-V2 20 CUN V2
## HCB01-V2 20 HCB V2
## HCB02-V2 20 HCB V2
## HCB03-V2 10 HCB V2
## HCB04-V2 10 HCB V2
## HCB06-V2 10 HCB V2
## HCB08-V2 10 HCB V2
## HCB09-V2 10 HCB V2
## HCB10-V2 10 HCB V2
## HCB12-V2 NA HCB V2
## HVA01-V2 20 HVA V2
## HVA02-V2 20 HVA V2
## HVA03-V2 10 HVA V2
## HVA04-V2 10 HVA V2
## HVR01-V2 10 HVR V2
## HVR02-V2 10 HVR V2
## ISBS01-V2 10 ISBS V2
## ISBS03-V2 10 ISBS V2
## ISBS06-V2 10 ISBS V2
## ISBS08-V2 20 ISBS V2
## ISBS09-V2 10 ISBS V2
## ISBS10-V2 NA ISBS V2
#Review the levels of the variable <<Respuesta_100>>
levels(sample_data(psv2)$Respuesta_100)
## [1] "PR" "sCR"
#Summary variable <<Respuesta_100>>
summary(sample_data(psv2)$Respuesta_100)
## PR sCR
## 6 17
list(sample_data(psv2)$Visit)
## [[1]]
## [1] "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2"
## [16] "V2" "V2" "V2" "V2" "V2" "V2" "V2" "V2"
#Filtered phyloseq object by patients visit <<V2>>
psv2
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 6023 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 6023 taxa by 6 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 6023 tips and 6021 internal nodes ]
#Taxonomic prevalence
# Show available ranks in the dataset
rank_names(psv2)
## [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus"
## Create table, number of features for each phyla
table(tax_table(psv2)[, "Phylum"], exclude = NULL)
##
## Actinobacteriota Bacteroidota Campylobacterota Cyanobacteria
## 622 749 6 9
## Desulfobacterota Euryarchaeota Firmicutes Fusobacteriota
## 21 8 4290 12
## Patescibacteria Proteobacteria Synergistota Verrucomicrobiota
## 18 246 12 30
#features were annotated with a Phylum of NA. These features are probably artifacts in a dataset like this, and should be removed.
psv2 <- subset_taxa(psv2, !is.na(Phylum) & !Phylum %in% c("", "uncharacterized"))
# compute prevalence for each feature and store it in a data frame
prevdf = apply(X = otu_table(psv2),
MARGIN = ifelse(taxa_are_rows(psv2), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
# add the taxonomy
prevdf = data.frame(Prevalence = prevdf,
TotalAbundance = taxa_sums(psv2),
tax_table(psv2))
plyr::ddply(prevdf, "Phylum", function(df1){cbind(mean(df1$Prevalence),sum(df1$Prevalence))}) -> dfprev
kable(dfprev)
| Phylum | 1 | 2 |
|---|---|---|
| Actinobacteriota | 1.1752412 | 731 |
| Bacteroidota | 1.2616822 | 945 |
| Campylobacterota | 0.8333333 | 5 |
| Cyanobacteria | 0.8888889 | 8 |
| Desulfobacterota | 1.1904762 | 25 |
| Euryarchaeota | 2.5000000 | 20 |
| Firmicutes | 1.2559441 | 5388 |
| Fusobacteriota | 0.5000000 | 6 |
| Patescibacteria | 0.8333333 | 15 |
| Proteobacteria | 1.1260163 | 277 |
| Synergistota | 1.0000000 | 12 |
| Verrucomicrobiota | 2.2666667 | 68 |
# Show available ranks in the dataset
rank_names(psv2)
## [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus"
## Create table, number of features for each phyla
table(tax_table(psv2)[, "Phylum"], exclude = NULL)
##
## Actinobacteriota Bacteroidota Campylobacterota Cyanobacteria
## 622 749 6 9
## Desulfobacterota Euryarchaeota Firmicutes Fusobacteriota
## 21 8 4290 12
## Patescibacteria Proteobacteria Synergistota Verrucomicrobiota
## 18 246 12 30
#features were annotated with a Phylum of NA. These features are probably artifacts in a dataset like this, and should be removed.
psv2 <- subset_taxa(psv2, !is.na(Phylum) & !Phylum %in% c("", "uncharacterized"))
# Computamos prevalencia para cada feature y la guardamos en un data frame
prevdf = apply(X = otu_table(psv2),
MARGIN = ifelse(taxa_are_rows(psv2), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
# Le agregamos la taxonomía
prevdf = data.frame(Prevalence = prevdf,
TotalAbundance = taxa_sums(psv2),
tax_table(psv2))
plyr::ddply(prevdf, "Phylum", function(df1){cbind(mean(df1$Prevalence),sum(df1$Prevalence))}) -> dfprev
kable(dfprev)
| Phylum | 1 | 2 |
|---|---|---|
| Actinobacteriota | 1.1752412 | 731 |
| Bacteroidota | 1.2616822 | 945 |
| Campylobacterota | 0.8333333 | 5 |
| Cyanobacteria | 0.8888889 | 8 |
| Desulfobacterota | 1.1904762 | 25 |
| Euryarchaeota | 2.5000000 | 20 |
| Firmicutes | 1.2559441 | 5388 |
| Fusobacteriota | 0.5000000 | 6 |
| Patescibacteria | 0.8333333 | 15 |
| Proteobacteria | 1.1260163 | 277 |
| Synergistota | 1.0000000 | 12 |
| Verrucomicrobiota | 2.2666667 | 68 |
#La columna 1 representa la media de read counts para ese Phylum, mientras que la columna 2 representa la suma
# Define phyla to filter
filterPhyla = c("Campylobacterota", "Cyanobacteria", "Halanaerobiaeota", "Fusobacteriota", "Synergistota" )
psv2 = subset_taxa(psv2, !Phylum %in% filterPhyla)
psv2
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 5984 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 5984 taxa by 6 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 5984 tips and 5982 internal nodes ]
#Filter taxa
# ilter taxa according to a threshold of mean number of _read counts_, in this case 1e-5
psd2 <- filter_taxa(psv2, function(x) mean(x) > 1e-5, TRUE)
# remove taxa that are not observed more than X times in at least 10% of the samples
psd3 <- filter_taxa(psd2, function(x) sum(x > 2) > (0.1*length(x)), TRUE)
# filter samples with less than 1000 reads
psd4 = prune_samples(sample_sums(psd3) > 1000, psd3)
psd4
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 557 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 557 taxa by 6 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 557 tips and 555 internal nodes ]
# We select the taxa of interest (ALLS)
prevdf1 = subset(prevdf, Phylum %in% get_taxa_unique(psd4, "Phylum"))
ggplot(prevdf1, aes(TotalAbundance, Prevalence / nsamples(psv2),color=Phylum)) +
# Agregamos una línea para nuestro umbral
geom_hline(yintercept = 0.05, alpha = 0.5, linetype = 2) + geom_point(size = 2, alpha = 0.7) +
scale_x_log10() + xlab("Total Abundance") + ylab("Prevalence [Frac. Samples]") +
facet_wrap(~Phylum) + theme(legend.position="none")
## Warning: Transformation introduced infinite values in continuous x-axis
# replace the sequences with a generic name
psd5 <- psd4
taxa_names(psd5) <- paste0("ASV", seq(ntaxa(psd5)))
head(taxa_names(psd5))
## [1] "ASV1" "ASV2" "ASV3" "ASV4" "ASV5" "ASV6"
#plot the distribution of read counts by sample
sample_sum_df <- data.frame(sum = sample_sums(psd5))
ggplot(sample_sum_df, aes(x = sum)) +
geom_histogram(color = "black", fill = "grey", binwidth = 2500) +
ggtitle("Distribution of sample sequencing depth") +
xlab("Read counts") +
theme(axis.title.y = element_blank())
#calculate rarefaction curves for each sample
# First load scripts
scripts <- c("graphical_methods.R",
"tree_methods.R",
"plot_merged_trees.R",
"specificity_methods.R",
"ternary_plot.R",
"richness.R",
"edgePCA.R",
"copy_number_correction.R",
"import_frogs.R",
"prevalence.R",
"compute_niche.R")
urls <- paste0("https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/R/", scripts)
for (url in urls) {
source(url)
}
## Loading required package: scales
##
## Attaching package: 'scales'
## The following object is masked from 'package:microbiome':
##
## alpha
## The following object is masked from 'package:purrr':
##
## discard
## The following object is masked from 'package:readr':
##
## col_factor
## Loading required package: vegan
## Loading required package: permute
## Warning: package 'permute' was built under R version 4.0.5
## Loading required package: lattice
## This is vegan 2.5-7
##
## Attaching package: 'vegan'
## The following object is masked from 'package:microbiome':
##
## diversity
#Plot the rarefaction curves
p <- ggrare(psd5, step = 100, color = "Respuesta_100", label = "SampleID", se = TRUE)
## rarefying sample CUN02-V2
## rarefying sample CUN03-V2
## rarefying sample HCB01-V2
## rarefying sample HCB02-V2
## rarefying sample HCB03-V2
## rarefying sample HCB04-V2
## rarefying sample HCB06-V2
## rarefying sample HCB08-V2
## rarefying sample HCB09-V2
## rarefying sample HCB10-V2
## rarefying sample HCB12-V2
## rarefying sample HVA01-V2
## rarefying sample HVA02-V2
## rarefying sample HVA03-V2
## rarefying sample HVA04-V2
## rarefying sample HVR01-V2
## rarefying sample HVR02-V2
## rarefying sample ISBS01-V2
## rarefying sample ISBS03-V2
## rarefying sample ISBS06-V2
## rarefying sample ISBS08-V2
## rarefying sample ISBS09-V2
## rarefying sample ISBS10-V2
(p <- p + facet_wrap(~Respuesta_100))
#For visualization in HTML format
library("kableExtra")
head(otu_table(psd5)) %>%
kable(format = "html", col.names = colnames(otu_table(psd5))) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "350px")
| ASV1 | ASV2 | ASV3 | ASV4 | ASV5 | ASV6 | ASV7 | ASV8 | ASV9 | ASV10 | ASV11 | ASV12 | ASV13 | ASV14 | ASV15 | ASV16 | ASV17 | ASV18 | ASV19 | ASV20 | ASV21 | ASV22 | ASV23 | ASV24 | ASV25 | ASV26 | ASV27 | ASV28 | ASV29 | ASV30 | ASV31 | ASV32 | ASV33 | ASV34 | ASV35 | ASV36 | ASV37 | ASV38 | ASV39 | ASV40 | ASV41 | ASV42 | ASV43 | ASV44 | ASV45 | ASV46 | ASV47 | ASV48 | ASV49 | ASV50 | ASV51 | ASV52 | ASV53 | ASV54 | ASV55 | ASV56 | ASV57 | ASV58 | ASV59 | ASV60 | ASV61 | ASV62 | ASV63 | ASV64 | ASV65 | ASV66 | ASV67 | ASV68 | ASV69 | ASV70 | ASV71 | ASV72 | ASV73 | ASV74 | ASV75 | ASV76 | ASV77 | ASV78 | ASV79 | ASV80 | ASV81 | ASV82 | ASV83 | ASV84 | ASV85 | ASV86 | ASV87 | ASV88 | ASV89 | ASV90 | ASV91 | ASV92 | ASV93 | ASV94 | ASV95 | ASV96 | ASV97 | ASV98 | ASV99 | ASV100 | ASV101 | ASV102 | ASV103 | ASV104 | ASV105 | ASV106 | ASV107 | ASV108 | ASV109 | ASV110 | ASV111 | ASV112 | ASV113 | ASV114 | ASV115 | ASV116 | ASV117 | ASV118 | ASV119 | ASV120 | ASV121 | ASV122 | ASV123 | ASV124 | ASV125 | ASV126 | ASV127 | ASV128 | ASV129 | ASV130 | ASV131 | ASV132 | ASV133 | ASV134 | ASV135 | ASV136 | ASV137 | ASV138 | ASV139 | ASV140 | ASV141 | ASV142 | ASV143 | ASV144 | ASV145 | ASV146 | ASV147 | ASV148 | ASV149 | ASV150 | ASV151 | ASV152 | ASV153 | ASV154 | ASV155 | ASV156 | ASV157 | ASV158 | ASV159 | ASV160 | ASV161 | ASV162 | ASV163 | ASV164 | ASV165 | ASV166 | ASV167 | ASV168 | ASV169 | ASV170 | ASV171 | ASV172 | ASV173 | ASV174 | ASV175 | ASV176 | ASV177 | ASV178 | ASV179 | ASV180 | ASV181 | ASV182 | ASV183 | ASV184 | ASV185 | ASV186 | ASV187 | ASV188 | ASV189 | ASV190 | ASV191 | ASV192 | ASV193 | ASV194 | ASV195 | ASV196 | ASV197 | ASV198 | ASV199 | ASV200 | ASV201 | ASV202 | ASV203 | ASV204 | ASV205 | ASV206 | ASV207 | ASV208 | ASV209 | ASV210 | ASV211 | ASV212 | ASV213 | ASV214 | ASV215 | ASV216 | ASV217 | ASV218 | ASV219 | ASV220 | ASV221 | ASV222 | ASV223 | ASV224 | ASV225 | ASV226 | ASV227 | ASV228 | ASV229 | ASV230 | ASV231 | ASV232 | ASV233 | ASV234 | ASV235 | ASV236 | ASV237 | ASV238 | ASV239 | ASV240 | ASV241 | ASV242 | ASV243 | ASV244 | ASV245 | ASV246 | ASV247 | ASV248 | ASV249 | ASV250 | ASV251 | ASV252 | ASV253 | ASV254 | ASV255 | ASV256 | ASV257 | ASV258 | ASV259 | ASV260 | ASV261 | ASV262 | ASV263 | ASV264 | ASV265 | ASV266 | ASV267 | ASV268 | ASV269 | ASV270 | ASV271 | ASV272 | ASV273 | ASV274 | ASV275 | ASV276 | ASV277 | ASV278 | ASV279 | ASV280 | ASV281 | ASV282 | ASV283 | ASV284 | ASV285 | ASV286 | ASV287 | ASV288 | ASV289 | ASV290 | ASV291 | ASV292 | ASV293 | ASV294 | ASV295 | ASV296 | ASV297 | ASV298 | ASV299 | ASV300 | ASV301 | ASV302 | ASV303 | ASV304 | ASV305 | ASV306 | ASV307 | ASV308 | ASV309 | ASV310 | ASV311 | ASV312 | ASV313 | ASV314 | ASV315 | ASV316 | ASV317 | ASV318 | ASV319 | ASV320 | ASV321 | ASV322 | ASV323 | ASV324 | ASV325 | ASV326 | ASV327 | ASV328 | ASV329 | ASV330 | ASV331 | ASV332 | ASV333 | ASV334 | ASV335 | ASV336 | ASV337 | ASV338 | ASV339 | ASV340 | ASV341 | ASV342 | ASV343 | ASV344 | ASV345 | ASV346 | ASV347 | ASV348 | ASV349 | ASV350 | ASV351 | ASV352 | ASV353 | ASV354 | ASV355 | ASV356 | ASV357 | ASV358 | ASV359 | ASV360 | ASV361 | ASV362 | ASV363 | ASV364 | ASV365 | ASV366 | ASV367 | ASV368 | ASV369 | ASV370 | ASV371 | ASV372 | ASV373 | ASV374 | ASV375 | ASV376 | ASV377 | ASV378 | ASV379 | ASV380 | ASV381 | ASV382 | ASV383 | ASV384 | ASV385 | ASV386 | ASV387 | ASV388 | ASV389 | ASV390 | ASV391 | ASV392 | ASV393 | ASV394 | ASV395 | ASV396 | ASV397 | ASV398 | ASV399 | ASV400 | ASV401 | ASV402 | ASV403 | ASV404 | ASV405 | ASV406 | ASV407 | ASV408 | ASV409 | ASV410 | ASV411 | ASV412 | ASV413 | ASV414 | ASV415 | ASV416 | ASV417 | ASV418 | ASV419 | ASV420 | ASV421 | ASV422 | ASV423 | ASV424 | ASV425 | ASV426 | ASV427 | ASV428 | ASV429 | ASV430 | ASV431 | ASV432 | ASV433 | ASV434 | ASV435 | ASV436 | ASV437 | ASV438 | ASV439 | ASV440 | ASV441 | ASV442 | ASV443 | ASV444 | ASV445 | ASV446 | ASV447 | ASV448 | ASV449 | ASV450 | ASV451 | ASV452 | ASV453 | ASV454 | ASV455 | ASV456 | ASV457 | ASV458 | ASV459 | ASV460 | ASV461 | ASV462 | ASV463 | ASV464 | ASV465 | ASV466 | ASV467 | ASV468 | ASV469 | ASV470 | ASV471 | ASV472 | ASV473 | ASV474 | ASV475 | ASV476 | ASV477 | ASV478 | ASV479 | ASV480 | ASV481 | ASV482 | ASV483 | ASV484 | ASV485 | ASV486 | ASV487 | ASV488 | ASV489 | ASV490 | ASV491 | ASV492 | ASV493 | ASV494 | ASV495 | ASV496 | ASV497 | ASV498 | ASV499 | ASV500 | ASV501 | ASV502 | ASV503 | ASV504 | ASV505 | ASV506 | ASV507 | ASV508 | ASV509 | ASV510 | ASV511 | ASV512 | ASV513 | ASV514 | ASV515 | ASV516 | ASV517 | ASV518 | ASV519 | ASV520 | ASV521 | ASV522 | ASV523 | ASV524 | ASV525 | ASV526 | ASV527 | ASV528 | ASV529 | ASV530 | ASV531 | ASV532 | ASV533 | ASV534 | ASV535 | ASV536 | ASV537 | ASV538 | ASV539 | ASV540 | ASV541 | ASV542 | ASV543 | ASV544 | ASV545 | ASV546 | ASV547 | ASV548 | ASV549 | ASV550 | ASV551 | ASV552 | ASV553 | ASV554 | ASV555 | ASV556 | ASV557 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CUN02-V2 | 0 | 0 | 528 | 0 | 0 | 0 | 447 | 1933 | 0 | 0 | 1863 | 272 | 0 | 213 | 0 | 0 | 1121 | 0 | 0 | 0 | 747 | 0 | 0 | 0 | 0 | 178 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 617 | 0 | 0 | 0 | 0 | 771 | 297 | 547 | 0 | 0 | 220 | 0 | 158 | 0 | 0 | 0 | 0 | 0 | 0 | 1099 | 718 | 334 | 110 | 188 | 0 | 0 | 0 | 0 | 0 | 745 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 380 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1020 | 397 | 0 | 120 | 0 | 264 | 0 | 128 | 135 | 0 | 0 | 0 | 0 | 0 | 1983 | 2081 | 0 | 127 | 0 | 449 | 0 | 0 | 0 | 0 | 0 | 490 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 121 | 445 | 106 | 643 | 0 | 0 | 0 | 0 | 85 | 0 | 484 | 0 | 0 | 0 | 375 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 303 | 0 | 0 | 117 | 79 | 381 | 0 | 0 | 0 | 0 | 0 | 42 | 75 | 0 | 143 | 0 | 459 | 1247 | 0 | 0 | 357 | 388 | 68 | 0 | 0 | 0 | 0 | 225 | 0 | 395 | 0 | 0 | 0 | 0 | 0 | 0 | 245 | 0 | 0 | 135 | 0 | 0 | 0 | 341 | 79 | 0 | 0 | 0 | 57 | 0 | 0 | 0 | 0 | 0 | 221 | 0 | 0 | 185 | 852 | 215 | 0 | 93 | 0 | 157 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0 | 0 | 0 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 0 | 0 | 204 | 0 | 90 | 0 | 0 | 44 | 0 | 118 | 217 | 0 | 0 | 0 | 0 | 0 | 104 | 0 | 0 | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 | 0 | 0 | 91 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 283 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 0 | 0 | 0 | 0 | 0 | 125 | 108 | 0 | 0 | 0 | 104 | 0 | 269 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 84 | 0 | 0 | 267 | 0 | 69 | 0 | 49 | 591 | 111 | 0 | 41 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 134 | 0 | 53 | 0 | 0 | 0 | 0 | 0 | 0 | 51 | 0 | 141 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 274 | 104 | 125 | 0 | 0 | 0 | 0 | 0 | 0 | 181 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 44 | 0 | 176 | 171 | 125 | 0 | 0 | 47 | 0 | 0 | 112 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 103 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 42 | 0 | 0 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 144 | 0 | 0 | 0 | 0 | 0 | 149 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 87 | 0 | 0 | 108 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 61 | 168 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0 | 0 | 0 | 0 | 0 | 46 | 0 | 0 | 87 | 0 | 0 | 0 | 0 | 59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| CUN03-V2 | 243 | 241 | 183 | 183 | 481 | 454 | 159 | 0 | 141 | 308 | 0 | 142 | 737 | 86 | 230 | 170 | 0 | 110 | 710 | 133 | 0 | 100 | 492 | 104 | 125 | 170 | 189 | 1340 | 0 | 312 | 432 | 83 | 76 | 1391 | 111 | 69 | 327 | 78 | 23 | 0 | 0 | 90 | 0 | 141 | 44 | 0 | 0 | 0 | 65 | 0 | 205 | 130 | 924 | 0 | 0 | 205 | 94 | 141 | 0 | 0 | 0 | 0 | 0 | 0 | 95 | 212 | 135 | 13 | 0 | 0 | 0 | 158 | 0 | 184 | 152 | 0 | 0 | 0 | 0 | 764 | 60 | 186 | 0 | 197 | 0 | 184 | 97 | 0 | 0 | 0 | 0 | 29 | 0 | 110 | 0 | 117 | 64 | 0 | 0 | 0 | 0 | 0 | 166 | 51 | 0 | 91 | 0 | 0 | 0 | 0 | 0 | 110 | 139 | 100 | 0 | 0 | 69 | 0 | 60 | 0 | 0 | 0 | 82 | 50 | 114 | 569 | 0 | 0 | 182 | 130 | 0 | 150 | 91 | 86 | 0 | 124 | 33 | 0 | 0 | 0 | 60 | 107 | 0 | 91 | 57 | 0 | 137 | 0 | 0 | 0 | 73 | 0 | 168 | 0 | 33 | 0 | 88 | 42 | 0 | 50 | 0 | 0 | 56 | 186 | 227 | 0 | 0 | 0 | 0 | 0 | 177 | 0 | 269 | 0 | 0 | 0 | 146 | 156 | 0 | 0 | 0 | 0 | 87 | 130 | 0 | 0 | 207 | 0 | 51 | 0 | 0 | 0 | 0 | 106 | 76 | 0 | 370 | 216 | 0 | 0 | 0 | 0 | 113 | 302 | 91 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 108 | 0 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 161 | 0 | 0 | 154 | 197 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 134 | 61 | 0 | 0 | 0 | 0 | 0 | 0 | 113 | 0 | 90 | 0 | 0 | 123 | 0 | 0 | 0 | 0 | 0 | 0 | 96 | 139 | 0 | 0 | 0 | 157 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 202 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0 | 0 | 0 | 0 | 0 | 104 | 0 | 58 | 0 | 46 | 108 | 0 | 0 | 83 | 0 | 160 | 0 | 123 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 102 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 97 | 13 | 0 | 0 | 113 | 68 | 86 | 0 | 0 | 0 | 74 | 0 | 0 | 0 | 0 | 145 | 0 | 106 | 0 | 0 | 0 | 0 | 115 | 0 | 57 | 0 | 0 | 0 | 0 | 83 | 58 | 0 | 83 | 0 | 0 | 0 | 0 | 85 | 0 | 12 | 0 | 84 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 61 | 0 | 0 | 0 | 180 | 0 | 0 | 59 | 0 | 0 | 0 | 57 | 0 | 0 | 0 | 0 | 178 | 0 | 146 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 187 | 141 | 0 | 0 | 83 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 32 | 0 | 0 | 37 | 149 | 0 | 0 | 0 | 97 | 0 | 0 | 93 | 0 | 114 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 119 | 0 | 51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 61 | 0 | 0 | 94 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 123 | 0 | 0 | 52 | 0 | 0 | 0 | 39 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 71 | 0 | 0 | 0 | 0 | 0 | 83 | 152 | 0 | 0 | 0 | 158 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 54 | 0 | 0 | 3 | 0 | 51 | 60 | 0 | 0 | 39 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 34 | 0 | 7 | 0 | 0 | 0 |
| HCB01-V2 | 131 | 136 | 408 | 119 | 0 | 0 | 372 | 0 | 59 | 0 | 0 | 271 | 0 | 220 | 0 | 300 | 0 | 58 | 0 | 352 | 0 | 363 | 0 | 215 | 310 | 247 | 0 | 1192 | 0 | 1145 | 0 | 170 | 0 | 1185 | 198 | 0 | 1078 | 0 | 0 | 0 | 0 | 535 | 0 | 223 | 0 | 0 | 0 | 0 | 193 | 182 | 774 | 498 | 854 | 0 | 0 | 229 | 279 | 107 | 0 | 88 | 0 | 0 | 638 | 249 | 0 | 248 | 90 | 617 | 187 | 0 | 55 | 83 | 79 | 221 | 205 | 0 | 0 | 0 | 0 | 706 | 406 | 154 | 0 | 634 | 0 | 218 | 225 | 0 | 0 | 214 | 0 | 0 | 0 | 117 | 0 | 139 | 296 | 0 | 223 | 0 | 0 | 0 | 114 | 220 | 0 | 66 | 0 | 0 | 0 | 0 | 0 | 0 | 87 | 0 | 0 | 376 | 141 | 0 | 0 | 144 | 89 | 168 | 179 | 0 | 0 | 449 | 0 | 0 | 114 | 0 | 190 | 149 | 140 | 63 | 122 | 444 | 110 | 0 | 0 | 0 | 0 | 81 | 0 | 0 | 0 | 0 | 0 | 139 | 0 | 0 | 106 | 0 | 0 | 0 | 62 | 71 | 82 | 175 | 155 | 139 | 0 | 0 | 0 | 0 | 0 | 115 | 89 | 0 | 141 | 130 | 0 | 0 | 0 | 0 | 0 | 0 | 107 | 78 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 0 | 0 | 79 | 0 | 114 | 0 | 0 | 0 | 101 | 303 | 0 | 0 | 0 | 82 | 0 | 0 | 310 | 0 | 316 | 172 | 0 | 0 | 0 | 64 | 0 | 0 | 304 | 0 | 0 | 41 | 0 | 0 | 0 | 294 | 221 | 0 | 0 | 101 | 0 | 0 | 0 | 0 | 223 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76 | 77 | 0 | 221 | 0 | 284 | 0 | 0 | 0 | 0 | 0 | 0 | 337 | 224 | 140 | 0 | 38 | 313 | 65 | 0 | 96 | 0 | 0 | 0 | 0 | 204 | 60 | 0 | 29 | 0 | 0 | 217 | 170 | 0 | 0 | 142 | 0 | 115 | 0 | 0 | 97 | 0 | 0 | 153 | 68 | 0 | 48 | 0 | 0 | 218 | 0 | 97 | 0 | 0 | 159 | 0 | 117 | 0 | 166 | 0 | 0 | 0 | 107 | 0 | 0 | 0 | 273 | 137 | 0 | 63 | 0 | 0 | 0 | 0 | 0 | 142 | 136 | 218 | 196 | 98 | 0 | 160 | 0 | 0 | 0 | 0 | 108 | 86 | 0 | 0 | 0 | 42 | 0 | 33 | 141 | 0 | 0 | 0 | 0 | 0 | 151 | 74 | 0 | 54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 267 | 0 | 0 | 0 | 0 | 73 | 95 | 27 | 0 | 0 | 0 | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 102 | 0 | 0 | 0 | 0 | 0 | 0 | 117 | 78 | 0 | 0 | 0 | 127 | 0 | 0 | 0 | 0 | 87 | 87 | 227 | 0 | 0 | 0 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | 109 | 0 | 97 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 62 | 0 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 85 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35 | 0 | 0 | 0 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 102 | 31 | 0 | 110 | 0 | 0 | 0 | 0 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 61 | 35 | 35 | 0 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 67 | 0 | 0 | 75 | 0 | 81 | 37 | 0 | 25 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 6 |
| HCB02-V2 | 250 | 0 | 0 | 0 | 0 | 0 | 0 | 413 | 54 | 0 | 396 | 62 | 0 | 0 | 0 | 0 | 277 | 0 | 0 | 0 | 244 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 124 | 0 | 0 | 0 | 0 | 39 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 177 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 273 | 308 | 548 | 114 | 0 | 0 | 0 | 0 | 0 | 112 | 0 | 0 | 563 | 0 | 28 | 0 | 0 | 0 | 106 | 86 | 191 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 196 | 0 | 0 | 0 | 0 | 207 | 0 | 0 | 0 | 349 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 62 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 135 | 0 | 0 | 127 | 0 | 0 | 0 | 259 | 0 | 0 | 0 | 0 | 0 | 101 | 64 | 0 | 0 | 0 | 0 | 0 | 0 | 239 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 457 | 0 | 0 | 0 | 0 | 80 | 0 | 0 | 249 | 0 | 0 | 0 | 0 | 0 | 0 | 1639 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 427 | 0 | 0 | 0 | 0 | 0 | 1462 | 120 | 0 | 0 | 0 | 0 | 0 | 0 | 51 | 0 | 0 | 0 | 54 | 0 | 116 | 0 | 0 | 15 | 0 | 0 | 179 | 0 | 0 | 105 | 0 | 0 | 0 | 0 | 0 | 152 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 81 | 0 | 0 | 0 | 0 | 90 | 304 | 116 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 112 | 0 | 879 | 0 | 29 | 0 | 0 | 138 | 0 | 0 | 0 | 0 | 129 | 0 | 0 | 0 | 0 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 196 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58 | 0 | 97 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 65 | 0 | 0 | 0 | 0 | 0 | 62 | 0 | 93 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 82 | 0 | 170 | 0 | 0 | 0 | 0 | 0 | 0 | 58 | 0 | 0 | 0 | 84 | 62 | 77 | 0 | 0 | 117 | 138 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 0 | 0 | 0 | 34 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60 | 0 | 96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 129 | 0 | 0 | 57 | 0 | 0 | 24 | 0 | 0 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 97 | 0 | 0 | 124 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 0 | 0 | 0 | 0 | 52 | 151 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| HCB03-V2 | 0 | 0 | 664 | 0 | 92 | 50 | 559 | 0 | 0 | 63 | 0 | 678 | 385 | 481 | 53 | 165 | 0 | 0 | 326 | 138 | 0 | 73 | 320 | 170 | 0 | 0 | 0 | 0 | 0 | 364 | 305 | 187 | 0 | 0 | 0 | 0 | 311 | 0 | 0 | 0 | 0 | 223 | 0 | 0 | 0 | 0 | 115 | 0 | 42 | 0 | 291 | 139 | 0 | 0 | 133 | 0 | 126 | 0 | 0 | 257 | 0 | 2237 | 0 | 156 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 227 | 269 | 111 | 2098 | 230 | 0 | 0 | 123 | 0 | 1841 | 220 | 170 | 0 | 0 | 0 | 95 | 0 | 113 | 0 | 91 | 0 | 0 | 268 | 177 | 0 | 83 | 72 | 67 | 0 | 481 | 0 | 179 | 0 | 1772 | 135 | 0 | 250 | 74 | 0 | 0 | 0 | 93 | 0 | 211 | 0 | 0 | 156 | 0 | 0 | 0 | 659 | 554 | 0 | 97 | 0 | 315 | 0 | 107 | 0 | 46 | 0 | 0 | 151 | 0 | 0 | 113 | 1967 | 0 | 0 | 169 | 0 | 624 | 0 | 0 | 89 | 0 | 0 | 434 | 0 | 0 | 59 | 0 | 0 | 0 | 163 | 108 | 67 | 0 | 0 | 0 | 77 | 0 | 161 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76 | 82 | 0 | 339 | 0 | 0 | 0 | 0 | 0 | 0 | 487 | 140 | 0 | 0 | 0 | 0 | 324 | 155 | 0 | 0 | 0 | 0 | 0 | 96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 276 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 135 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 106 | 37 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 104 | 312 | 0 | 0 | 0 | 43 | 0 | 0 | 0 | 280 | 0 | 0 | 0 | 0 | 0 | 72 | 0 | 0 | 0 | 0 | 91 | 0 | 0 | 106 | 24 | 0 | 0 | 0 | 0 | 0 | 166 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 80 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 0 | 0 | 0 | 130 | 0 | 0 | 0 | 180 | 0 | 92 | 0 | 0 | 0 | 0 | 0 | 64 | 0 | 63 | 62 | 0 | 51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 95 | 0 | 0 | 0 | 0 | 144 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 154 | 0 | 13 | 0 | 0 | 65 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 86 | 0 | 117 | 373 | 0 | 0 | 0 | 140 | 0 | 0 | 0 | 0 | 134 | 0 | 0 | 0 | 88 | 0 | 0 | 0 | 0 | 0 | 51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 84 | 0 | 0 | 0 | 0 | 0 | 0 | 123 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 83 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 136 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 10 | 0 |
| HCB04-V2 | 2261 | 2120 | 3782 | 1573 | 465 | 503 | 3665 | 0 | 1219 | 342 | 0 | 2588 | 0 | 2120 | 285 | 702 | 0 | 887 | 0 | 636 | 0 | 215 | 0 | 502 | 174 | 198 | 212 | 0 | 0 | 0 | 0 | 414 | 564 | 0 | 144 | 563 | 0 | 225 | 0 | 0 | 807 | 0 | 0 | 180 | 177 | 0 | 319 | 0 | 89 | 206 | 0 | 0 | 0 | 679 | 282 | 0 | 0 | 84 | 149 | 1018 | 0 | 0 | 0 | 0 | 118 | 0 | 154 | 0 | 141 | 46 | 0 | 0 | 108 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 184 | 0 | 0 | 0 | 0 | 242 | 535 | 144 | 0 | 107 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 229 | 0 | 116 | 0 | 157 | 0 | 0 | 439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 174 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 94 | 82 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 173 | 0 | 0 | 0 | 0 | 0 | 325 | 68 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 306 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 74 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 172 | 121 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 73 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 83 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 255 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 242 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 112 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 146 | 0 | 0 | 62 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 54 | 106 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 113 | 0 | 0 | 0 | 0 | 0 | 51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 47 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 |
#taxonomy table
head(tax_table(psd5)) %>%
kable(format = "html", col.names = colnames(tax_table(psd5))) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "320px")
| Kingdom | Phylum | Class | Order | Family | Genus | |
|---|---|---|---|---|---|---|
| ASV1 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Streptococcaceae | Streptococcus |
| ASV2 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Streptococcaceae | Streptococcus |
| ASV3 | Bacteria | Firmicutes | Clostridia | Lachnospirales | Lachnospiraceae | Blautia |
| ASV4 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Streptococcaceae | Streptococcus |
| ASV5 | Bacteria | Proteobacteria | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia-Shigella |
| ASV6 | Bacteria | Proteobacteria | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia-Shigella |
#metadata table
as(sample_data(psd5), "data.frame") -> metad
metad %>%
kable(format = "html", col.names = colnames(metad)) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "400px")
| SampleID | Edad_a_la_inclusion | Sexo | Respuesta_100 | aloTPH_previo_si_no | Citogenética_alto_riesgo | Afect_Extramedular_PET_screening_si_no | Dias_producción | CRS_si_no | Grado_máx_CRS | HOSPITAL | Visit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CUN02-V2 | CUN02-V2 | 43 | MUJER | sCR | NO | NO | SI | 120 | SI | 10 | CUN | V2 |
| CUN03-V2 | CUN03-V2 | 45 | HOMBRE | sCR | NO | SI | NO | 100 | SI | 20 | CUN | V2 |
| HCB01-V2 | HCB01-V2 | 66 | HOMBRE | PR | NO | NO | SI | 110 | SI | 20 | HCB | V2 |
| HCB02-V2 | HCB02-V2 | 66 | HOMBRE | PR | NO | SI | NO | 110 | SI | 20 | HCB | V2 |
| HCB03-V2 | HCB03-V2 | 60 | HOMBRE | sCR | NO | SI | SI | 110 | SI | 10 | HCB | V2 |
| HCB04-V2 | HCB04-V2 | 55 | MUJER | sCR | NO | NO | NO | 110 | SI | 10 | HCB | V2 |
| HCB06-V2 | HCB06-V2 | 73 | MUJER | sCR | NO | NO | NO | 90 | SI | 10 | HCB | V2 |
| HCB08-V2 | HCB08-V2 | 62 | MUJER | PR | NO | NO | SI | 100 | SI | 10 | HCB | V2 |
| HCB09-V2 | HCB09-V2 | 53 | HOMBRE | sCR | NO | NO | NO | 90 | SI | 10 | HCB | V2 |
| HCB10-V2 | HCB10-V2 | 52 | HOMBRE | sCR | SI | NO | SI | 100 | SI | 10 | HCB | V2 |
| HCB12-V2 | HCB12-V2 | 64 | HOMBRE | sCR | NO | SI | NO | 110 | NO | NA | HCB | V2 |
| HVA01-V2 | HVA01-V2 | 58 | HOMBRE | PR | NO | NO | NO | 120 | SI | 20 | HVA | V2 |
| HVA02-V2 | HVA02-V2 | 67 | HOMBRE | sCR | NO | NO | SI | 100 | SI | 20 | HVA | V2 |
| HVA03-V2 | HVA03-V2 | 61 | HOMBRE | sCR | SI | NO | SI | 140 | SI | 10 | HVA | V2 |
| HVA04-V2 | HVA04-V2 | 74 | MUJER | sCR | NO | NO | NO | 100 | SI | 10 | HVA | V2 |
| HVR01-V2 | HVR01-V2 | 65 | HOMBRE | sCR | NO | SI | 110 | SI | 10 | HVR | V2 | |
| HVR02-V2 | HVR02-V2 | 46 | HOMBRE | sCR | NO | SI | 120 | SI | 10 | HVR | V2 | |
| ISBS01-V2 | ISBS01-V2 | 56 | HOMBRE | sCR | NO | SI | SI | 120 | SI | 10 | ISBS | V2 |
| ISBS03-V2 | ISBS03-V2 | 70 | MUJER | sCR | NO | SI | 100 | SI | 10 | ISBS | V2 | |
| ISBS06-V2 | ISBS06-V2 | 60 | HOMBRE | PR | NO | NO | NO | 130 | SI | 10 | ISBS | V2 |
| ISBS08-V2 | ISBS08-V2 | 53 | HOMBRE | sCR | NO | SI | 110 | SI | 20 | ISBS | V2 | |
| ISBS09-V2 | ISBS09-V2 | 65 | MUJER | PR | NO | NO | SI | 100 | SI | 10 | ISBS | V2 |
| ISBS10-V2 | ISBS10-V2 | 68 | HOMBRE | sCR | NO | SI | SI | 140 | NO | NA | ISBS | V2 |
#phylogenetic tree
plot_tree(psd5, method = "treeonly", ladderize = "left")
##Global summary
library(phyloseq)
library(microbiome)
summarize_phyloseq(psd5)
## Compositional = NO2
## 1] Min. number of reads = 146862] Max. number of reads = 1009443] Total number of reads = 7463814] Average number of reads = 32451.3478260875] Median number of reads = 329137] Sparsity = 0.7430333307314036] Any OTU sum to 1 or less? NO8] Number of singletons = 09] Percent of OTUs that are singletons
## (i.e. exactly one read detected across all samples)010] Number of sample variables are: 12SampleIDEdad_a_la_inclusionSexoRespuesta_100aloTPH_previo_si_noCitogenética_alto_riesgoAfect_Extramedular_PET_screening_si_noDias_producciónCRS_si_noGrado_máx_CRSHOSPITALVisit2
## [[1]]
## [1] "1] Min. number of reads = 14686"
##
## [[2]]
## [1] "2] Max. number of reads = 100944"
##
## [[3]]
## [1] "3] Total number of reads = 746381"
##
## [[4]]
## [1] "4] Average number of reads = 32451.347826087"
##
## [[5]]
## [1] "5] Median number of reads = 32913"
##
## [[6]]
## [1] "7] Sparsity = 0.743033330731403"
##
## [[7]]
## [1] "6] Any OTU sum to 1 or less? NO"
##
## [[8]]
## [1] "8] Number of singletons = 0"
##
## [[9]]
## [1] "9] Percent of OTUs that are singletons \n (i.e. exactly one read detected across all samples)0"
##
## [[10]]
## [1] "10] Number of sample variables are: 12"
##
## [[11]]
## [1] "SampleID"
## [2] "Edad_a_la_inclusion"
## [3] "Sexo"
## [4] "Respuesta_100"
## [5] "aloTPH_previo_si_no"
## [6] "Citogenética_alto_riesgo"
## [7] "Afect_Extramedular_PET_screening_si_no"
## [8] "Dias_producción"
## [9] "CRS_si_no"
## [10] "Grado_máx_CRS"
## [11] "HOSPITAL"
## [12] "Visit"
#group all tables
df <- psmelt(psd5)
head(df) %>%
kable(format = "html", col.names = colnames(df)) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "400px")
| OTU | Sample | Abundance | SampleID | Edad_a_la_inclusion | Sexo | Respuesta_100 | aloTPH_previo_si_no | Citogenética_alto_riesgo | Afect_Extramedular_PET_screening_si_no | Dias_producción | CRS_si_no | Grado_máx_CRS | HOSPITAL | Visit | Kingdom | Phylum | Class | Order | Family | Genus | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4871 | ASV29 | ISBS03-V2 | 4933 | ISBS03-V2 | 70 | MUJER | sCR | NO | SI | 100 | SI | 10 | ISBS | V2 | Bacteria | Verrucomicrobiota | Verrucomicrobiae | Verrucomicrobiales | Akkermansiaceae | Akkermansia | |
| 7426 | ASV39 | ISBS03-V2 | 4171 | ISBS03-V2 | 70 | MUJER | sCR | NO | SI | 100 | SI | 10 | ISBS | V2 | Bacteria | Verrucomicrobiota | Verrucomicrobiae | Verrucomicrobiales | Akkermansiaceae | Akkermansia | |
| 7702 | ASV40 | ISBS03-V2 | 4020 | ISBS03-V2 | 70 | MUJER | sCR | NO | SI | 100 | SI | 10 | ISBS | V2 | Bacteria | Verrucomicrobiota | Verrucomicrobiae | Verrucomicrobiales | Akkermansiaceae | Akkermansia | |
| 9466 | ASV47 | ISBS03-V2 | 3935 | ISBS03-V2 | 70 | MUJER | sCR | NO | SI | 100 | SI | 10 | ISBS | V2 | Bacteria | Actinobacteriota | Actinobacteria | Bifidobacteriales | Bifidobacteriaceae | Bifidobacterium | |
| 5117 | ASV3 | HCB04-V2 | 3782 | HCB04-V2 | 55 | MUJER | sCR | NO | NO | NO | 110 | SI | 10 | HCB | V2 | Bacteria | Firmicutes | Clostridia | Lachnospirales | Lachnospiraceae | Blautia |
| 12070 | ASV7 | HCB04-V2 | 3665 | HCB04-V2 | 55 | MUJER | sCR | NO | NO | NO | 110 | SI | 10 | HCB | V2 | Bacteria | Firmicutes | Clostridia | Lachnospirales | Lachnospiraceae | Blautia |
##Distribution of the samples according to the metadata <
res <- plot_frequencies(sample_data(psd5), "Respuesta_100", "Sexo")
print(res$plot)
## <environment: 0x55d58a33a180>
# En formato tabla
res$data %>%
kable(format = "html", col.names = colnames(res$data), digits = 2) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "300px")
| Groups | Factor | n | pct | .group |
|---|---|---|---|---|
| PR | HOMBRE | 4 | 66.67 | 1 |
| PR | MUJER | 2 | 33.33 | 1 |
| sCR | HOMBRE | 12 | 70.59 | 2 |
| sCR | MUJER | 5 | 29.41 | 2 |
#Taxa
taxa_sums(psd5)
## ASV1 ASV2 ASV3 ASV4 ASV5 ASV6 ASV7 ASV8 ASV9 ASV10 ASV11
## 16001 14310 12410 10325 8156 7768 10920 6885 7832 6216 6528
## ASV12 ASV13 ASV14 ASV15 ASV16 ASV17 ASV18 ASV19 ASV20 ASV21 ASV22
## 8577 8130 7045 4841 7658 4986 5973 7514 6463 3963 4877
## ASV23 ASV24 ASV25 ASV26 ASV27 ASV28 ASV29 ASV30 ASV31 ASV32 ASV33
## 5876 5391 4502 5060 3177 4933 6493 3813 4751 4545 3698
## ASV34 ASV35 ASV36 ASV37 ASV38 ASV39 ASV40 ASV41 ASV42 ASV43 ASV44
## 4569 3529 3521 3332 5288 5702 5637 1072 3452 2834 3858
## ASV45 ASV46 ASV47 ASV48 ASV49 ASV50 ASV51 ASV52 ASV53 ASV54 ASV55
## 4591 5149 4864 3673 2666 3375 2728 2836 3312 790 4453
## ASV56 ASV57 ASV58 ASV59 ASV60 ASV61 ASV62 ASV63 ASV64 ASV65 ASV66
## 2917 2955 3666 3608 2976 3338 3337 895 3947 1999 2945
## ASV67 ASV68 ASV69 ASV70 ASV71 ASV72 ASV73 ASV74 ASV75 ASV76 ASV77
## 3708 978 2811 2929 2802 2219 2466 2719 2789 2544 2847
## ASV78 ASV79 ASV80 ASV81 ASV82 ASV83 ASV84 ASV85 ASV86 ASV87 ASV88
## 2556 1805 2649 2276 2034 2762 2174 3522 2308 2454 1718
## ASV89 ASV90 ASV91 ASV92 ASV93 ASV94 ASV95 ASV96 ASV97 ASV98 ASV99
## 2236 1975 2689 2876 2240 2675 2609 2291 2035 2622 3095
## ASV100 ASV101 ASV102 ASV103 ASV104 ASV105 ASV106 ASV107 ASV108 ASV109 ASV110
## 2188 2879 2822 2208 1884 2089 2419 2361 2883 2181 2027
## ASV111 ASV112 ASV113 ASV114 ASV115 ASV116 ASV117 ASV118 ASV119 ASV120 ASV121
## 2262 2030 1472 2159 1794 604 1872 2710 1977 1938 2244
## ASV122 ASV123 ASV124 ASV125 ASV126 ASV127 ASV128 ASV129 ASV130 ASV131 ASV132
## 1437 1796 1399 1424 1923 1731 1890 1624 2010 2427 1658
## ASV133 ASV134 ASV135 ASV136 ASV137 ASV138 ASV139 ASV140 ASV141 ASV142 ASV143
## 1685 1927 1616 1365 2071 1738 1619 2087 1682 1331 1698
## ASV144 ASV145 ASV146 ASV147 ASV148 ASV149 ASV150 ASV151 ASV152 ASV153 ASV154
## 1830 1299 2257 1729 1157 1938 1722 1478 2136 1593 1551
## ASV155 ASV156 ASV157 ASV158 ASV159 ASV160 ASV161 ASV162 ASV163 ASV164 ASV165
## 1506 1932 1472 1405 1950 1312 1842 2161 245 1310 1885
## ASV166 ASV167 ASV168 ASV169 ASV170 ASV171 ASV172 ASV173 ASV174 ASV175 ASV176
## 1255 1699 1639 781 710 1575 1572 1849 1417 1260 1379
## ASV177 ASV178 ASV179 ASV180 ASV181 ASV182 ASV183 ASV184 ASV185 ASV186 ASV187
## 1244 1303 1896 1577 1249 1758 958 1137 951 1170 1079
## ASV188 ASV189 ASV190 ASV191 ASV192 ASV193 ASV194 ASV195 ASV196 ASV197 ASV198
## 1294 1326 1470 1223 1042 1733 1057 1014 1705 1231 1483
## ASV199 ASV200 ASV201 ASV202 ASV203 ASV204 ASV205 ASV206 ASV207 ASV208 ASV209
## 167 1158 1308 1371 1176 1113 1299 629 1045 628 678
## ASV210 ASV211 ASV212 ASV213 ASV214 ASV215 ASV216 ASV217 ASV218 ASV219 ASV220
## 615 697 981 1096 496 1072 923 1114 525 843 1174
## ASV221 ASV222 ASV223 ASV224 ASV225 ASV226 ASV227 ASV228 ASV229 ASV230 ASV231
## 1057 569 1058 809 435 1348 775 1176 1314 496 776
## ASV232 ASV233 ASV234 ASV235 ASV236 ASV237 ASV238 ASV239 ASV240 ASV241 ASV242
## 1282 901 494 700 769 874 407 712 912 826 562
## ASV243 ASV244 ASV245 ASV246 ASV247 ASV248 ASV249 ASV250 ASV251 ASV252 ASV253
## 484 1036 698 506 465 1179 778 1111 438 843 1004
## ASV254 ASV255 ASV256 ASV257 ASV258 ASV259 ASV260 ASV261 ASV262 ASV263 ASV264
## 645 760 636 1035 742 861 699 816 856 769 749
## ASV265 ASV266 ASV267 ASV268 ASV269 ASV270 ASV271 ASV272 ASV273 ASV274 ASV275
## 843 383 820 687 475 458 548 1043 569 611 499
## ASV276 ASV277 ASV278 ASV279 ASV280 ASV281 ASV282 ASV283 ASV284 ASV285 ASV286
## 436 648 988 919 728 365 604 448 547 654 938
## ASV287 ASV288 ASV289 ASV290 ASV291 ASV292 ASV293 ASV294 ASV295 ASV296 ASV297
## 542 579 651 526 418 360 538 382 913 326 709
## ASV298 ASV299 ASV300 ASV301 ASV302 ASV303 ASV304 ASV305 ASV306 ASV307 ASV308
## 264 355 656 686 544 582 753 501 689 579 406
## ASV309 ASV310 ASV311 ASV312 ASV313 ASV314 ASV315 ASV316 ASV317 ASV318 ASV319
## 492 354 725 372 661 317 574 303 470 683 682
## ASV320 ASV321 ASV322 ASV323 ASV324 ASV325 ASV326 ASV327 ASV328 ASV329 ASV330
## 650 423 540 719 453 408 603 471 422 142 686
## ASV331 ASV332 ASV333 ASV334 ASV335 ASV336 ASV337 ASV338 ASV339 ASV340 ASV341
## 225 305 456 647 499 424 270 247 599 424 466
## ASV342 ASV343 ASV344 ASV345 ASV346 ASV347 ASV348 ASV349 ASV350 ASV351 ASV352
## 445 463 325 562 585 334 335 295 559 426 660
## ASV353 ASV354 ASV355 ASV356 ASV357 ASV358 ASV359 ASV360 ASV361 ASV362 ASV363
## 507 536 562 583 144 633 455 540 425 223 462
## ASV364 ASV365 ASV366 ASV367 ASV368 ASV369 ASV370 ASV371 ASV372 ASV373 ASV374
## 347 501 536 406 405 511 314 415 301 395 341
## ASV375 ASV376 ASV377 ASV378 ASV379 ASV380 ASV381 ASV382 ASV383 ASV384 ASV385
## 158 269 404 226 252 396 543 457 531 385 301
## ASV386 ASV387 ASV388 ASV389 ASV390 ASV391 ASV392 ASV393 ASV394 ASV395 ASV396
## 521 329 385 345 503 502 413 493 343 357 488
## ASV397 ASV398 ASV399 ASV400 ASV401 ASV402 ASV403 ASV404 ASV405 ASV406 ASV407
## 137 139 296 305 401 394 462 318 306 279 233
## ASV408 ASV409 ASV410 ASV411 ASV412 ASV413 ASV414 ASV415 ASV416 ASV417 ASV418
## 330 227 192 444 281 436 350 282 430 340 330
## ASV419 ASV420 ASV421 ASV422 ASV423 ASV424 ASV425 ASV426 ASV427 ASV428 ASV429
## 427 297 358 336 256 307 161 229 349 325 193
## ASV430 ASV431 ASV432 ASV433 ASV434 ASV435 ASV436 ASV437 ASV438 ASV439 ASV440
## 380 273 344 392 245 284 277 163 356 260 248
## ASV441 ASV442 ASV443 ASV444 ASV445 ASV446 ASV447 ASV448 ASV449 ASV450 ASV451
## 369 259 187 284 279 359 359 250 356 355 126
## ASV452 ASV453 ASV454 ASV455 ASV456 ASV457 ASV458 ASV459 ASV460 ASV461 ASV462
## 349 179 347 224 208 249 220 215 195 263 215
## ASV463 ASV464 ASV465 ASV466 ASV467 ASV468 ASV469 ASV470 ASV471 ASV472 ASV473
## 229 330 245 245 299 197 323 265 264 213 200
## ASV474 ASV475 ASV476 ASV477 ASV478 ASV479 ASV480 ASV481 ASV482 ASV483 ASV484
## 85 176 253 182 308 246 300 99 131 294 262
## ASV485 ASV486 ASV487 ASV488 ASV489 ASV490 ASV491 ASV492 ASV493 ASV494 ASV495
## 286 284 284 278 246 185 272 159 266 80 263
## ASV496 ASV497 ASV498 ASV499 ASV500 ASV501 ASV502 ASV503 ASV504 ASV505 ASV506
## 178 257 90 250 205 143 188 245 91 124 159
## ASV507 ASV508 ASV509 ASV510 ASV511 ASV512 ASV513 ASV514 ASV515 ASV516 ASV517
## 236 201 233 169 88 228 71 221 179 18 175
## ASV518 ASV519 ASV520 ASV521 ASV522 ASV523 ASV524 ASV525 ASV526 ASV527 ASV528
## 206 129 201 138 178 193 132 189 106 188 188
## ASV529 ASV530 ASV531 ASV532 ASV533 ASV534 ASV535 ASV536 ASV537 ASV538 ASV539
## 156 162 147 154 154 147 120 65 128 121 99
## ASV540 ASV541 ASV542 ASV543 ASV544 ASV545 ASV546 ASV547 ASV548 ASV549 ASV550
## 108 104 103 68 100 26 90 55 75 80 65
## ASV551 ASV552 ASV553 ASV554 ASV555 ASV556 ASV557
## 54 50 42 36 35 34 23
kable(tax_table(psd5)[,6], format = "markdown")
| Genus | |
|---|---|
| ASV1 | Streptococcus |
| ASV2 | Streptococcus |
| ASV3 | Blautia |
| ASV4 | Streptococcus |
| ASV5 | Escherichia-Shigella |
| ASV6 | Escherichia-Shigella |
| ASV7 | Blautia |
| ASV8 | Ruminococcus |
| ASV9 | Streptococcus |
| ASV10 | Escherichia-Shigella |
| ASV11 | Ruminococcus |
| ASV12 | Blautia |
| ASV13 | Collinsella |
| ASV14 | Blautia |
| ASV15 | Escherichia-Shigella |
| ASV16 | Bifidobacterium |
| ASV17 | Ruminococcus |
| ASV18 | Streptococcus |
| ASV19 | Collinsella |
| ASV20 | Bifidobacterium |
| ASV21 | Ruminococcus |
| ASV22 | Streptococcus |
| ASV23 | Collinsella |
| ASV24 | Bifidobacterium |
| ASV25 | Streptococcus |
| ASV26 | Anaerostipes |
| ASV27 | Escherichia-Shigella |
| ASV28 | Ruminococcus |
| ASV29 | Akkermansia |
| ASV30 | Subdoligranulum |
| ASV31 | Collinsella |
| ASV32 | Bifidobacterium |
| ASV33 | Streptococcus |
| ASV34 | Ruminococcus |
| ASV35 | Streptococcus |
| ASV36 | Streptococcus |
| ASV37 | Subdoligranulum |
| ASV38 | Streptococcus |
| ASV39 | Akkermansia |
| ASV40 | Akkermansia |
| ASV41 | Streptococcus |
| ASV42 | Blautia |
| ASV43 | Ruminococcus |
| ASV44 | Anaerostipes |
| ASV45 | Streptococcus |
| ASV46 | Akkermansia |
| ASV47 | Bifidobacterium |
| ASV48 | Ligilactobacillus |
| ASV49 | Streptococcus |
| ASV50 | Anaerostipes |
| ASV51 | Subdoligranulum |
| ASV52 | Blautia |
| ASV53 | Ruminococcus |
| ASV54 | Streptococcus |
| ASV55 | Bifidobacterium |
| ASV56 | Bacteroides |
| ASV57 | Blautia |
| ASV58 | Streptococcus |
| ASV59 | Streptococcus |
| ASV60 | Blautia |
| ASV61 | Ligilactobacillus |
| ASV62 | Holdemanella |
| ASV63 | HT002 |
| ASV64 | Blautia |
| ASV65 | Escherichia-Shigella |
| ASV66 | Bacteroides |
| ASV67 | Streptococcus |
| ASV68 | HT002 |
| ASV69 | Anaerostipes |
| ASV70 | Enterococcus |
| ASV71 | Enterococcus |
| ASV72 | Bacteroides |
| ASV73 | [Ruminococcus] gnavus group |
| ASV74 | [Eubacterium] hallii group |
| ASV75 | [Eubacterium] hallii group |
| ASV76 | Dorea |
| ASV77 | Holdemanella |
| ASV78 | Bifidobacterium |
| ASV79 | Ruminococcus |
| ASV80 | Ruminococcus |
| ASV81 | Blautia |
| ASV82 | Bacteroides |
| ASV83 | Holdemanella |
| ASV84 | Subdoligranulum |
| ASV85 | Bifidobacterium |
| ASV86 | Bacteroides |
| ASV87 | Blautia |
| ASV88 | Ruminococcus |
| ASV89 | Dorea |
| ASV90 | Erysipelatoclostridium |
| ASV91 | NA |
| ASV92 | Streptococcus |
| ASV93 | Subdoligranulum |
| ASV94 | [Ruminococcus] gnavus group |
| ASV95 | Ligilactobacillus |
| ASV96 | [Eubacterium] hallii group |
| ASV97 | Blautia |
| ASV98 | Agathobacter |
| ASV99 | Blautia |
| ASV100 | Subdoligranulum |
| ASV101 | Bacteroides |
| ASV102 | Bacteroides |
| ASV103 | NA |
| ASV104 | Erysipelatoclostridium |
| ASV105 | Bifidobacterium |
| ASV106 | Streptococcus |
| ASV107 | Holdemanella |
| ASV108 | Bifidobacterium |
| ASV109 | Enterococcus |
| ASV110 | Bifidobacterium |
| ASV111 | NA |
| ASV112 | Faecalibacterium |
| ASV113 | Bacteroides |
| ASV114 | Agathobacter |
| ASV115 | Subdoligranulum |
| ASV116 | HT002 |
| ASV117 | [Eubacterium] hallii group |
| ASV118 | Akkermansia |
| ASV119 | Agathobacter |
| ASV120 | [Eubacterium] hallii group |
| ASV121 | Bacteroides |
| ASV122 | Erysipelatoclostridium |
| ASV123 | Blautia |
| ASV124 | Bifidobacterium |
| ASV125 | Bifidobacterium |
| ASV126 | Ruminococcus |
| ASV127 | Dorea |
| ASV128 | Ligilactobacillus |
| ASV129 | NA |
| ASV130 | Faecalibacterium |
| ASV131 | Blautia |
| ASV132 | Bacteroides |
| ASV133 | Fusicatenibacter |
| ASV134 | Streptococcus |
| ASV135 | Romboutsia |
| ASV136 | Subdoligranulum |
| ASV137 | Dorea |
| ASV138 | Enterococcus |
| ASV139 | Subdoligranulum |
| ASV140 | Dialister |
| ASV141 | Faecalibacterium |
| ASV142 | Bacteroides |
| ASV143 | Bifidobacterium |
| ASV144 | Faecalibacterium |
| ASV145 | Bifidobacterium |
| ASV146 | Akkermansia |
| ASV147 | Streptococcus |
| ASV148 | Erysipelatoclostridium |
| ASV149 | Bacteroides |
| ASV150 | Blautia |
| ASV151 | NA |
| ASV152 | Bacteroides |
| ASV153 | [Ruminococcus] torques group |
| ASV154 | NA |
| ASV155 | [Ruminococcus] gnavus group |
| ASV156 | Blautia |
| ASV157 | Fusicatenibacter |
| ASV158 | [Eubacterium] hallii group |
| ASV159 | Blautia |
| ASV160 | Blautia |
| ASV161 | Bacteroides |
| ASV162 | Bacteroides |
| ASV163 | Lacticaseibacillus |
| ASV164 | Bacteroides |
| ASV165 | Bacteroides |
| ASV166 | [Eubacterium] hallii group |
| ASV167 | Dorea |
| ASV168 | Agathobacter |
| ASV169 | Lactobacillus |
| ASV170 | Lactobacillus |
| ASV171 | Streptococcus |
| ASV172 | Blautia |
| ASV173 | Bacteroides |
| ASV174 | Blautia |
| ASV175 | Dorea |
| ASV176 | NA |
| ASV177 | Fusicatenibacter |
| ASV178 | NA |
| ASV179 | Klebsiella |
| ASV180 | Bacteroides |
| ASV181 | [Ruminococcus] torques group |
| ASV182 | Akkermansia |
| ASV183 | Faecalibacterium |
| ASV184 | Faecalibacterium |
| ASV185 | Bifidobacterium |
| ASV186 | Blautia |
| ASV187 | Bacteroides |
| ASV188 | Ligilactobacillus |
| ASV189 | Blautia |
| ASV190 | Dorea |
| ASV191 | Coprococcus |
| ASV192 | [Eubacterium] hallii group |
| ASV193 | Klebsiella |
| ASV194 | Romboutsia |
| ASV195 | Faecalibacterium |
| ASV196 | Akkermansia |
| ASV197 | Ruminococcus |
| ASV198 | Bacteroides |
| ASV199 | Lacticaseibacillus |
| ASV200 | [Ruminococcus] gnavus group |
| ASV201 | [Clostridium] innocuum group |
| ASV202 | Bacteroides |
| ASV203 | Faecalibacterium |
| ASV204 | Ruminococcus |
| ASV205 | Streptococcus |
| ASV206 | Alistipes |
| ASV207 | Lachnoclostridium |
| ASV208 | Limosilactobacillus |
| ASV209 | Enterococcus |
| ASV210 | Enterococcus |
| ASV211 | Fusicatenibacter |
| ASV212 | Coprococcus |
| ASV213 | Blautia |
| ASV214 | Alistipes |
| ASV215 | Streptococcus |
| ASV216 | [Clostridium] innocuum group |
| ASV217 | Dorea |
| ASV218 | Limosilactobacillus |
| ASV219 | [Ruminococcus] torques group |
| ASV220 | Anaerostipes |
| ASV221 | Streptococcus |
| ASV222 | NA |
| ASV223 | Blautia |
| ASV224 | Alistipes |
| ASV225 | Lactobacillus |
| ASV226 | Bacteroides |
| ASV227 | Alistipes |
| ASV228 | Bacteroides |
| ASV229 | NA |
| ASV230 | NA |
| ASV231 | Faecalibacterium |
| ASV232 | NA |
| ASV233 | Ligilactobacillus |
| ASV234 | Enterococcus |
| ASV235 | Eggerthella |
| ASV236 | Bacteroides |
| ASV237 | [Ruminococcus] torques group |
| ASV238 | Lactobacillus |
| ASV239 | Romboutsia |
| ASV240 | Blautia |
| ASV241 | Streptococcus |
| ASV242 | Faecalibacterium |
| ASV243 | Collinsella |
| ASV244 | UCG-002 |
| ASV245 | [Ruminococcus] torques group |
| ASV246 | Erysipelatoclostridium |
| ASV247 | Limosilactobacillus |
| ASV248 | Klebsiella |
| ASV249 | Alistipes |
| ASV250 | Streptococcus |
| ASV251 | Collinsella |
| ASV252 | Streptococcus |
| ASV253 | Blautia |
| ASV254 | Romboutsia |
| ASV255 | Streptococcus |
| ASV256 | Parabacteroides |
| ASV257 | Methanobrevibacter |
| ASV258 | UBA1819 |
| ASV259 | [Ruminococcus] torques group |
| ASV260 | Coprococcus |
| ASV261 | UCG-002 |
| ASV262 | Blautia |
| ASV263 | NA |
| ASV264 | Hespellia |
| ASV265 | Anaerostipes |
| ASV266 | Enterococcus |
| ASV267 | Roseburia |
| ASV268 | Coprococcus |
| ASV269 | Ruminococcus |
| ASV270 | NA |
| ASV271 | Streptococcus |
| ASV272 | NA |
| ASV273 | Blautia |
| ASV274 | Monoglobus |
| ASV275 | Sellimonas |
| ASV276 | Faecalibacterium |
| ASV277 | [Eubacterium] hallii group |
| ASV278 | Streptococcus |
| ASV279 | Senegalimassilia |
| ASV280 | Bacteroides |
| ASV281 | Collinsella |
| ASV282 | Bifidobacterium |
| ASV283 | Eggerthella |
| ASV284 | Lachnoclostridium |
| ASV285 | UBA1819 |
| ASV286 | Bacteroides |
| ASV287 | Lachnoclostridium |
| ASV288 | Parabacteroides |
| ASV289 | Butyricicoccus |
| ASV290 | Veillonella |
| ASV291 | Erysipelatoclostridium |
| ASV292 | Collinsella |
| ASV293 | Erysipelatoclostridium |
| ASV294 | NA |
| ASV295 | Bifidobacterium |
| ASV296 | Alistipes |
| ASV297 | Bacteroides |
| ASV298 | Limosilactobacillus |
| ASV299 | Faecalibacterium |
| ASV300 | Bifidobacterium |
| ASV301 | Bacteroides |
| ASV302 | [Clostridium] innocuum group |
| ASV303 | Haemophilus |
| ASV304 | Streptococcus |
| ASV305 | Sellimonas |
| ASV306 | Roseburia |
| ASV307 | Alistipes |
| ASV308 | Eggerthella |
| ASV309 | Monoglobus |
| ASV310 | Lachnoclostridium |
| ASV311 | Bacteroides |
| ASV312 | Lachnoclostridium |
| ASV313 | Dorea |
| ASV314 | NA |
| ASV315 | Streptococcus |
| ASV316 | NA |
| ASV317 | NA |
| ASV318 | UCG-002 |
| ASV319 | Hespellia |
| ASV320 | Bacteroides |
| ASV321 | [Eubacterium] hallii group |
| ASV322 | Clostridium sensu stricto 1 |
| ASV323 | Methanobrevibacter |
| ASV324 | Alistipes |
| ASV325 | Anaerostipes |
| ASV326 | Bacteroides |
| ASV327 | Monoglobus |
| ASV328 | Parabacteroides |
| ASV329 | Subdoligranulum |
| ASV330 | Senegalimassilia |
| ASV331 | Erysipelatoclostridium |
| ASV332 | NA |
| ASV333 | Alistipes |
| ASV334 | Coprococcus |
| ASV335 | [Eubacterium] hallii group |
| ASV336 | Streptococcus |
| ASV337 | Veillonella |
| ASV338 | NA |
| ASV339 | Streptococcus |
| ASV340 | Coprococcus |
| ASV341 | Bifidobacterium |
| ASV342 | Lachnoclostridium |
| ASV343 | [Clostridium] innocuum group |
| ASV344 | Lactococcus |
| ASV345 | Bacteroides |
| ASV346 | Blautia |
| ASV347 | Bacteroides |
| ASV348 | Lachnoclostridium |
| ASV349 | NA |
| ASV350 | UCG-002 |
| ASV351 | Veillonella |
| ASV352 | Haemophilus |
| ASV353 | Roseburia |
| ASV354 | Coprococcus |
| ASV355 | Methanobrevibacter |
| ASV356 | Streptococcus |
| ASV357 | Lactococcus |
| ASV358 | Bacteroides |
| ASV359 | Christensenellaceae R-7 group |
| ASV360 | Roseburia |
| ASV361 | Anaerostipes |
| ASV362 | NA |
| ASV363 | UBA1819 |
| ASV364 | Faecalibacterium |
| ASV365 | Bacteroides |
| ASV366 | Senegalimassilia |
| ASV367 | [Ruminococcus] torques group |
| ASV368 | [Eubacterium] hallii group |
| ASV369 | Blautia |
| ASV370 | Veillonella |
| ASV371 | Bacteroides |
| ASV372 | [Eubacterium] hallii group |
| ASV373 | Bacteroides |
| ASV374 | Alistipes |
| ASV375 | Lachnoclostridium |
| ASV376 | Family XIII AD3011 group |
| ASV377 | Bacteroides |
| ASV378 | [Eubacterium] hallii group |
| ASV379 | Eggerthella |
| ASV380 | Subdoligranulum |
| ASV381 | Blautia |
| ASV382 | Bacteroides |
| ASV383 | Coprococcus |
| ASV384 | NA |
| ASV385 | Bifidobacterium |
| ASV386 | [Ruminococcus] torques group |
| ASV387 | Erysipelatoclostridium |
| ASV388 | UCG-005 |
| ASV389 | Flavonifractor |
| ASV390 | Bacteroides |
| ASV391 | Bacteroides |
| ASV392 | Subdoligranulum |
| ASV393 | [Ruminococcus] gauvreauii group |
| ASV394 | Christensenellaceae R-7 group |
| ASV395 | NA |
| ASV396 | Streptococcus |
| ASV397 | Lachnoclostridium |
| ASV398 | Sellimonas |
| ASV399 | Anaerostipes |
| ASV400 | Blautia |
| ASV401 | Incertae Sedis |
| ASV402 | Coprococcus |
| ASV403 | Lachnospiraceae NK4A136 group |
| ASV404 | Sellimonas |
| ASV405 | Monoglobus |
| ASV406 | Phascolarctobacterium |
| ASV407 | Incertae Sedis |
| ASV408 | Slackia |
| ASV409 | [Eubacterium] brachy group |
| ASV410 | Bacteroides |
| ASV411 | Christensenellaceae R-7 group |
| ASV412 | Agathobacter |
| ASV413 | Blautia |
| ASV414 | Senegalimassilia |
| ASV415 | NA |
| ASV416 | Hespellia |
| ASV417 | Alistipes |
| ASV418 | Streptococcus |
| ASV419 | Eubacterium |
| ASV420 | UCG-002 |
| ASV421 | NA |
| ASV422 | Clostridium sensu stricto 1 |
| ASV423 | Collinsella |
| ASV424 | Streptococcus |
| ASV425 | NA |
| ASV426 | Family XIII AD3011 group |
| ASV427 | Bacteroides |
| ASV428 | Butyricicoccus |
| ASV429 | Erysipelatoclostridium |
| ASV430 | Coprococcus |
| ASV431 | NA |
| ASV432 | Veillonella |
| ASV433 | NA |
| ASV434 | Family XIII AD3011 group |
| ASV435 | Bacteroides |
| ASV436 | UCG-005 |
| ASV437 | Incertae Sedis |
| ASV438 | Streptococcus |
| ASV439 | Blautia |
| ASV440 | Bacteroides |
| ASV441 | Incertae Sedis |
| ASV442 | [Ruminococcus] gauvreauii group |
| ASV443 | Eggerthella |
| ASV444 | Bacteroides |
| ASV445 | Clostridium sensu stricto 1 |
| ASV446 | Coprococcus |
| ASV447 | UBA1819 |
| ASV448 | Bacteroides |
| ASV449 | Blautia |
| ASV450 | Blautia |
| ASV451 | [Eubacterium] siraeum group |
| ASV452 | Bacteroides |
| ASV453 | Alistipes |
| ASV454 | Christensenellaceae R-7 group |
| ASV455 | Gemella |
| ASV456 | Intestinibacter |
| ASV457 | Incertae Sedis |
| ASV458 | NA |
| ASV459 | Sellimonas |
| ASV460 | Streptococcus |
| ASV461 | Roseburia |
| ASV462 | Subdoligranulum |
| ASV463 | Bifidobacterium |
| ASV464 | Lachnoclostridium |
| ASV465 | Parabacteroides |
| ASV466 | Blautia |
| ASV467 | Incertae Sedis |
| ASV468 | Roseburia |
| ASV469 | Alistipes |
| ASV470 | Anaerostipes |
| ASV471 | Slackia |
| ASV472 | Christensenellaceae R-7 group |
| ASV473 | NK4A214 group |
| ASV474 | Coprobacillus |
| ASV475 | NA |
| ASV476 | Oscillibacter |
| ASV477 | Faecalibacterium |
| ASV478 | [Eubacterium] eligens group |
| ASV479 | Streptococcus |
| ASV480 | Intestinimonas |
| ASV481 | Latilactobacillus |
| ASV482 | NA |
| ASV483 | Subdoligranulum |
| ASV484 | Blautia |
| ASV485 | Parabacteroides |
| ASV486 | Christensenellaceae R-7 group |
| ASV487 | NA |
| ASV488 | Olsenella |
| ASV489 | Oscillibacter |
| ASV490 | NA |
| ASV491 | Lachnoclostridium |
| ASV492 | Roseburia |
| ASV493 | Parabacteroides |
| ASV494 | Latilactobacillus |
| ASV495 | Family XIII AD3011 group |
| ASV496 | NA |
| ASV497 | Blautia |
| ASV498 | Flavonifractor |
| ASV499 | Bacteroides |
| ASV500 | DTU089 |
| ASV501 | Odoribacter |
| ASV502 | CAG-56 |
| ASV503 | Christensenellaceae R-7 group |
| ASV504 | Blautia |
| ASV505 | Streptococcus |
| ASV506 | Roseburia |
| ASV507 | UCG-002 |
| ASV508 | Incertae Sedis |
| ASV509 | Terrisporobacter |
| ASV510 | Oscillibacter |
| ASV511 | Lachnoclostridium |
| ASV512 | Lachnospiraceae NK4A136 group |
| ASV513 | Lachnoclostridium |
| ASV514 | Anaerostipes |
| ASV515 | Incertae Sedis |
| ASV516 | Solobacterium |
| ASV517 | Alistipes |
| ASV518 | [Ruminococcus] torques group |
| ASV519 | Intestinimonas |
| ASV520 | Christensenellaceae R-7 group |
| ASV521 | [Eubacterium] siraeum group |
| ASV522 | Family XIII AD3011 group |
| ASV523 | Christensenellaceae R-7 group |
| ASV524 | Streptococcus |
| ASV525 | Bacteroides |
| ASV526 | Family XIII AD3011 group |
| ASV527 | Coprobacillus |
| ASV528 | UBA1819 |
| ASV529 | Oscillibacter |
| ASV530 | Christensenellaceae R-7 group |
| ASV531 | NK4A214 group |
| ASV532 | Incertae Sedis |
| ASV533 | [Eubacterium] nodatum group |
| ASV534 | UC5-1-2E3 |
| ASV535 | Christensenellaceae R-7 group |
| ASV536 | Family XIII UCG-001 |
| ASV537 | NA |
| ASV538 | Alistipes |
| ASV539 | [Eubacterium] brachy group |
| ASV540 | Lachnospira |
| ASV541 | Holdemania |
| ASV542 | Bilophila |
| ASV543 | Scardovia |
| ASV544 | Family XIII AD3011 group |
| ASV545 | TM7x |
| ASV546 | NA |
| ASV547 | Anaerotruncus |
| ASV548 | Actinomyces |
| ASV549 | Solobacterium |
| ASV550 | NA |
| ASV551 | Lachnospiraceae FCS020 group |
| ASV552 | Turicibacter |
| ASV553 | Bifidobacterium |
| ASV554 | NA |
| ASV555 | UCG-005 |
| ASV556 | Parabacteroides |
| ASV557 | Candidatus Soleaferrea |
#ASVs of the same gender at the sequence level are different even though they correspond to the same gender. Visualization of sequences of the taxonomic range in phylogenetic trees
# cluster by gender
psd5.genus = tax_glom(psd5, "Genus", NArm = FALSE)
# Luego por altura en el árbol filogenético
h1 = 0.4
psd5.tip = tip_glom(psd5, h = h1)
# plot a comparison to visualize the differences
multiPlotTitleTextSize = 15
p2tree = plot_tree(psd5, method = "treeonly",
ladderize = "left",
title = "Sin aglomeración") +
theme(plot.title = element_text(size = multiPlotTitleTextSize))
p3tree = plot_tree(psd5.genus, method = "treeonly",
ladderize = "left", title = "A nivel de género") +
theme(plot.title = element_text(size = multiPlotTitleTextSize))
p4tree = plot_tree(psd5.tip, method = "treeonly",
ladderize = "left", title = "Por altura") +
theme(plot.title = element_text(size = multiPlotTitleTextSize))
# plot all trees
grid.arrange(nrow = 1, p2tree, p3tree, p4tree)
####DIVERSITY ANALYSIS####
#Richness Summary Graphics
plot_richness(psd5)
## Warning in estimate_richness(physeq, split = TRUE, measures = measures): The data you have provided does not have
## any singletons. This is highly suspicious. Results of richness
## estimates (for example) are probably unreliable, or wrong, if you have already
## trimmed low-abundance taxa from the data.
##
## We recommended that you find the un-trimmed data and retry.
#Richness Summary Graphics group by <<Respuesta_100>>
(p = plot_richness(psd5, x = "Respuesta_100"))
## Warning in estimate_richness(physeq, split = TRUE, measures = measures): The data you have provided does not have
## any singletons. This is highly suspicious. Results of richness
## estimates (for example) are probably unreliable, or wrong, if you have already
## trimmed low-abundance taxa from the data.
##
## We recommended that you find the un-trimmed data and retry.
#Richness Summary Graphics group by <<Respuesta_100>>
plot_richness(psd5, color = "Respuesta_100", x = "Respuesta_100", measures = c("Observed", "Chao1", "Shannon")) + geom_boxplot(aes(fill = Respuesta_100), alpha=.7) + scale_color_manual(values = c("#a6cee3", "#b2df8a")) + scale_fill_manual(values = c("#a6cee3", "#b2df8a"))
## Warning in estimate_richness(physeq, split = TRUE, measures = measures): The data you have provided does not have
## any singletons. This is highly suspicious. Results of richness
## estimates (for example) are probably unreliable, or wrong, if you have already
## trimmed low-abundance taxa from the data.
##
## We recommended that you find the un-trimmed data and retry.
#Check if there is any significant effect of alpha diversity based on response type
# save a dataframe with the alpha diversity measures
alpha_pd <- estimate_pd(psd5)
## Calculating Faiths PD-index...
# combine the metadata with alpha.diversity
data <- cbind(sample_data(psd5), alpha_pd)
# calculate an ANOVA
psd5.anova <- aov(PD ~ Respuesta_100, data)
# install.packages("xtable")
library(xtable)
psd5.anova.table <- xtable(psd5.anova)
kable(psd5.anova.table, caption = "Tabla ANOVA", digits = 5, format = "markdown")
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Respuesta_100 | 1 | 1.38598 | 1.38598 | 0.0554 | 0.81619 |
| Residuals | 21 | 525.32500 | 25.01548 | NA | NA |
#Mann-Whitney-Wilcoxon Test
psd5.wilcox <- wilcox.test(PD ~ Respuesta_100, data)
psd5.wilcox
##
## Wilcoxon rank sum exact test
##
## data: PD by Respuesta_100
## W = 57, p-value = 0.7077
## alternative hypothesis: true location shift is not equal to 0
#Tablle of divsersity scores
tab <- global(psd5, index = "all")
## Warning: The microbiome::global function is deprecated.
## Use the function microbiome::alpha instead.
## Observed richness
## Other forms of richness
## Diversity
## Evenness
## Dominance
## Rarity
head(tab) %>%
kable(format = "html", col.names = colnames(tab), digits = 2) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "300px")
| observed | chao1 | diversity_inverse_simpson | diversity_gini_simpson | diversity_shannon | diversity_fisher | diversity_coverage | evenness_camargo | evenness_pielou | evenness_simpson | evenness_evar | evenness_bulla | dominance_dbp | dominance_dmn | dominance_absolute | dominance_relative | dominance_simpson | dominance_core_abundance | dominance_gini | rarity_log_modulo_skewness | rarity_low_abundance | rarity_rare_abundance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CUN02-V2 | 121 | 121 | 44.17 | 0.98 | 4.23 | 15.62 | 17 | 0.50 | 0.88 | 0.37 | 0.45 | 0.57 | 0.06 | 0.11 | 2081 | 0.06 | 0.02 | 0.07 | 0.90 | 1.59 | 0.03 | 0.24 |
| CUN03-V2 | 188 | 188 | 76.19 | 0.99 | 4.82 | 27.05 | 37 | 0.50 | 0.92 | 0.41 | 0.59 | 0.68 | 0.05 | 0.10 | 1391 | 0.05 | 0.01 | 0.24 | 0.82 | 1.74 | 0.04 | 0.25 |
| HCB01-V2 | 193 | 193 | 89.62 | 0.99 | 4.87 | 26.84 | 37 | 0.51 | 0.93 | 0.46 | 0.57 | 0.66 | 0.03 | 0.07 | 1192 | 0.03 | 0.01 | 0.16 | 0.81 | 1.65 | 0.06 | 0.31 |
| HCB02-V2 | 88 | 88 | 29.97 | 0.97 | 3.94 | 12.28 | 13 | 0.77 | 0.88 | 0.34 | 0.55 | 0.61 | 0.10 | 0.19 | 1639 | 0.10 | 0.03 | 0.04 | 0.92 | 1.76 | 0.01 | 0.58 |
| HCB03-V2 | 126 | 126 | 36.74 | 0.97 | 4.20 | 16.75 | 15 | 0.49 | 0.87 | 0.29 | 0.49 | 0.58 | 0.07 | 0.14 | 2237 | 0.07 | 0.03 | 0.21 | 0.90 | 1.67 | 0.02 | 0.19 |
| HCB04-V2 | 83 | 83 | 22.14 | 0.95 | 3.61 | 10.14 | 8 | 0.33 | 0.82 | 0.27 | 0.32 | 0.49 | 0.10 | 0.20 | 3782 | 0.10 | 0.05 | 0.66 | 0.95 | 1.59 | 0.01 | 0.10 |
library(microbiome)
# Richness
tab <- richness(psd5)
# Dominance
tab <- dominance(psd5, index = "all")
# Rarity
tab <- rarity(psd5, index = "all")
# Coverage
tab <- coverage(psd5, threshold = 0.5)
# inequality
tab <- inequality(psd5)
# evennes
tab <- evenness(psd5, "all")
library(ggpubr)
# Generate a `phyloseq` object without taxa that adds 0 reads
psd5.2 <- prune_taxa(taxa_sums(psd5) > 0, psd5)
# calculate the diversity indices
tab <- diversities(psd5.2, index = "all")
## Warning: 'diversities' is deprecated.
## Use 'diversity' instead.
## See help("Deprecated") and help("The microbiome::diversities function has been
## replaced by function microbiome::alpha. Update your code accordingly.-deprecated").
## Observed richness
## Other forms of richness
## Diversity
## Evenness
## Dominance
## Rarity
# visualize the results table
head(tab) %>%
kable(format = "html", col.names = colnames(tab), digits = 2) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "310px")
| observed | chao1 | diversity_inverse_simpson | diversity_gini_simpson | diversity_shannon | diversity_fisher | diversity_coverage | evenness_camargo | evenness_pielou | evenness_simpson | evenness_evar | evenness_bulla | dominance_dbp | dominance_dmn | dominance_absolute | dominance_relative | dominance_simpson | dominance_core_abundance | dominance_gini | rarity_log_modulo_skewness | rarity_low_abundance | rarity_rare_abundance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CUN02-V2 | 121 | 121 | 44.17 | 0.98 | 4.23 | 15.62 | 17 | 0.50 | 0.88 | 0.37 | 0.45 | 0.57 | 0.06 | 0.11 | 2081 | 0.06 | 0.02 | 0.07 | 0.90 | 1.59 | 0.03 | 0.24 |
| CUN03-V2 | 188 | 188 | 76.19 | 0.99 | 4.82 | 27.05 | 37 | 0.50 | 0.92 | 0.41 | 0.59 | 0.68 | 0.05 | 0.10 | 1391 | 0.05 | 0.01 | 0.24 | 0.82 | 1.74 | 0.04 | 0.25 |
| HCB01-V2 | 193 | 193 | 89.62 | 0.99 | 4.87 | 26.84 | 37 | 0.51 | 0.93 | 0.46 | 0.57 | 0.66 | 0.03 | 0.07 | 1192 | 0.03 | 0.01 | 0.16 | 0.81 | 1.65 | 0.06 | 0.31 |
| HCB02-V2 | 88 | 88 | 29.97 | 0.97 | 3.94 | 12.28 | 13 | 0.77 | 0.88 | 0.34 | 0.55 | 0.61 | 0.10 | 0.19 | 1639 | 0.10 | 0.03 | 0.04 | 0.92 | 1.76 | 0.01 | 0.58 |
| HCB03-V2 | 126 | 126 | 36.74 | 0.97 | 4.20 | 16.75 | 15 | 0.49 | 0.87 | 0.29 | 0.49 | 0.58 | 0.07 | 0.14 | 2237 | 0.07 | 0.03 | 0.21 | 0.90 | 1.67 | 0.02 | 0.19 |
| HCB04-V2 | 83 | 83 | 22.14 | 0.95 | 3.61 | 10.14 | 8 | 0.33 | 0.82 | 0.27 | 0.32 | 0.49 | 0.10 | 0.20 | 3782 | 0.10 | 0.05 | 0.66 | 0.95 | 1.59 | 0.01 | 0.10 |
#extract metadata
psd5.2.meta <- meta(psd5.2)
head(psd5.2.meta) %>%
kable(format = "html", col.names = colnames(psd5.2.meta), digits = 2) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "500px")
| SampleID | Edad_a_la_inclusion | Sexo | Respuesta_100 | aloTPH_previo_si_no | Citogenética_alto_riesgo | Afect_Extramedular_PET_screening_si_no | Dias_producción | CRS_si_no | Grado_máx_CRS | HOSPITAL | Visit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CUN02-V2 | CUN02-V2 | 43 | MUJER | sCR | NO | NO | SI | 120 | SI | 10 | CUN | V2 |
| CUN03-V2 | CUN03-V2 | 45 | HOMBRE | sCR | NO | SI | NO | 100 | SI | 20 | CUN | V2 |
| HCB01-V2 | HCB01-V2 | 66 | HOMBRE | PR | NO | NO | SI | 110 | SI | 20 | HCB | V2 |
| HCB02-V2 | HCB02-V2 | 66 | HOMBRE | PR | NO | SI | NO | 110 | SI | 20 | HCB | V2 |
| HCB03-V2 | HCB03-V2 | 60 | HOMBRE | sCR | NO | SI | SI | 110 | SI | 10 | HCB | V2 |
| HCB04-V2 | HCB04-V2 | 55 | MUJER | sCR | NO | NO | NO | 110 | SI | 10 | HCB | V2 |
#add the diversity table to the metadata
psd5.2.meta$Shannon <- tab$diversity_shannon
# get the variables from our `phyloseq` object
rpp <- levels(psd5.2.meta$Respuesta_100)
#Create a list of what we want to compare
pares.rpp <- combn(seq_along(rpp), 2, simplify = FALSE, FUN = function(i)rpp[i])
# Print results
print(pares.rpp)
## [[1]]
## [1] "PR" "sCR"
#Plot
p1 <- ggviolin(psd5.2.meta, x = "Respuesta_100", y = "Shannon",
add = "boxplot", fill = "Respuesta_100", palette = c("#a6cee3", "#b2df8a"))
print(p1)
#Plot with compare means
p1 <- p1 + stat_compare_means(comparisons = pares.rpp)
print(p1)
###Beta diversity
#UniFrac
psd5.mds.unifrac <- ordinate(psd5, method = "MDS", distance = "unifrac")
## Warning in UniFrac(physeq, ...): Randomly assigning root as -- ASV415 -- in the
## phylogenetic tree in the data you provided.
evals <- psd5.mds.unifrac$values$Eigenvalues
pord1 <- plot_ordination(psd5, psd5.mds.unifrac, color = "Respuesta_100") +
labs(col = "Respuesta_100") +
coord_fixed(sqrt(evals[2] / evals[1]))
#Bray-Curtis
psd5.mds.bray <- ordinate(psd5, method = "MDS", distance = "bray")
evals <- psd5.mds.bray$values$Eigenvalues
pord2 <- plot_ordination(psd5, psd5.mds.bray, color = "Respuesta_100") +
labs(col = "Respuesta_100") +
coord_fixed(sqrt(evals[2] / evals[1]))
grid.arrange(pord1, pord2)
#DPCoA
psd5.dpcoa.unifrac <- ordinate(psd5, method = "DPCoA", distance = "dpcoa")
evals <- psd5.dpcoa.unifrac$eig
pord3 <- plot_ordination(psd5, psd5.dpcoa.unifrac, color = "Respuesta_100", shape = "Sexo") +
labs(col = "Respuesta_100") +
coord_fixed(sqrt(evals[2] / evals[1])) +
scale_color_manual(values = c("#a6cee3", "#b2df8a")) +
scale_fill_manual(values = c("#a6cee3", "#b2df8a")) +
geom_point(size=4)
## Warning: non-unique values when setting 'row.names':
## Species coordinates not found directly in ordination object. Attempting weighted average (`vegan::wascores`)
pord3
#multidimensional scaling
library(tsnemicrobiota)
tsne_res <- tsne_phyloseq(psd5, distance= "dpcoa", perplexity = 8, verbose=0, rng_seed = 3901)
# Plot
pord4 <- plot_tsne_phyloseq(psd5, tsne_res, color = "Respuesta_100", shape = "Sexo") +
geom_point(size=4) +
scale_color_manual(values = c("#a6cee3", "#b2df8a")) +
scale_fill_manual(values = c("#a6cee3", "#b2df8a"))
grid.arrange(pord3, pord4)
## Warning: Use of `plot_df[[axes[1]]]` is discouraged. Use `.data[[axes[1]]]`
## instead.
## Warning: Use of `plot_df[[axes[2]]]` is discouraged. Use `.data[[axes[2]]]`
## instead.
## Warning: Use of `plot_df[[axes[1]]]` is discouraged. Use `.data[[axes[1]]]`
## instead.
## Warning: Use of `plot_df[[axes[2]]]` is discouraged. Use `.data[[axes[2]]]`
## instead.
###Abundance analysis
# Necesitamos obtener las taxa más abundantes, en este caso el top 15
top15 <- get_top_taxa(physeq_obj = psd5, n = 15, relative = T,
discard_other = T, other_label = "Other")
# Ya que no todas las taxa fueron clasificadas a nivel de especie, generamos etiquetas compuestas de distintos rangos taxonómicos para el gráfico
top15 <- name_taxa(top15, label = "", species = F, other_label = "Other")
# Finalmente graficamos
fantaxtic_bar(top15, color_by = "Family", label_by = "Genus", facet_by = NULL, grid_by = NULL, other_color = "Grey") -> ptop15
## Level N.color.shades Central.color
## 1 Streptococcaceae 4 #6495ed
## 2 Lachnospiraceae 4 #ff7256
## 3 Enterobacteriaceae 2 #edbc64
## 4 Coriobacteriaceae 3 #8470ff
## 5 Bifidobacteriaceae 2 #8ee5ee
#Barplot of abundance
ptop15
#Groupo by Respuesta_100
fantaxtic_bar(top15, color_by = "Family", label_by = "Genus", facet_by = "Respuesta_100", grid_by = NULL, other_color = "Grey") -> ptop15.2
## Level N.color.shades Central.color
## 1 Streptococcaceae 4 #6495ed
## 2 Lachnospiraceae 4 #ff7256
## 3 Enterobacteriaceae 2 #edbc64
## 4 Coriobacteriaceae 3 #8470ff
## 5 Bifidobacteriaceae 2 #8ee5ee
#Plot
ptop15.2
library(ampvis2)
# extract the read counts table and the taxonomy table from the psd5 object
# generate a copy so as not to overwrite psd5
obj <- psd5
# change the orientation of the otu_table
t(otu_table(obj)) -> otu_table(obj)
# Extraemos las tablas
otutable <- data.frame(OTU = rownames(phyloseq::otu_table(obj)@.Data),
phyloseq::otu_table(obj)@.Data,
phyloseq::tax_table(obj)@.Data,
check.names = FALSE
)
# extract la metadada
metadata <- data.frame(phyloseq::sample_data(obj),
check.names = FALSE
)
#ampvis2 requires that 1) the taxonomic ranges are seven and go from Kingdom to Species and 2) the first column of the metadata is the identifier of each sample
# Then we duplicate the Genus column and rename it to Species
otutable$Species = otutable$Genus
# Reordenamos la metadata
metadata <- metadata[,c("SampleID", "Edad_a_la_inclusion", "Sexo", "Respuesta_100", "aloTPH_previo_si_no", "Citogenética_alto_riesgo", "Afect_Extramedular_PET_screening_si_no", "Dias_producción", "CRS_si_no", "Grado_máx_CRS", "HOSPITAL", "Visit")]
# generate the ampvis object
av2 <- amp_load(otutable, metadata)
#estructura de las comunidades en las curvas de abundancia
amp_rankabundance(av2, log10_x = T, group_by = "Sexo")
#estructura de las comunidades en las curvas de abundancia
amp_rankabundance(av2, log10_x = T, group_by = "Respuesta_100")
#estructura de las comunidades en las curvas de abundancia
amp_rankabundance(av2, log10_x = T, group_by = "HOSPITAL")
#heatmap of abundance
amp_heatmap(av2,
group_by = "Respuesta_100",
facet_by = "Sexo",
plot_values = TRUE,
tax_show = 20,
tax_aggregate = "Genus",
tax_add = "Phylum",
plot_colorscale = "sqrt",
plot_legendbreaks = c(1, 5, 10)
)
#Box Plots.
amp_boxplot(av2,
group_by = "Respuesta_100",
tax_show = 20,
tax_aggregate = "Genus",
tax_add = "Phylum",
adjust_zero = T,
plot_log = T) +
scale_color_manual(values = c("#a6cee3", "#b2df8a")) +
scale_fill_manual(values = c("#a6cee3", "#b2df8a"))
#Venn Diagram
# some of these microorganisms are shared between all samples
amp_venn(av2, group_by = "Respuesta_100", cut_a = 0, cut_f = 50, text_size = 3)
#Differential abundance analysis
#we want to determine exactly which taxa is more represented in one condition versus another and to what extent. DESeq2 package,
# Create a DESeq2 object with the `phyloseq_to_deseq2` function
diagdds = phyloseq_to_deseq2(psd4, ~Respuesta_100)
## converting counts to integer mode
# We calculate the size factors as part of the normalization of the samples
# calculate geometric means prior to estimate size factors
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
# We normalize and perform the parametric Wald test to determine differentially abundant taxa.
diagdds = DESeq(diagdds, test="Wald", fitType="local")
## using pre-existing size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## -- replacing outliers and refitting for 297 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
# Create a DESeq2 object with the `phyloseq_to_deseq2` function
diagdds = phyloseq_to_deseq2(psd5, ~Respuesta_100)
## converting counts to integer mode
# We calculate the size factors as part of the normalization of the samples
# calculate geometric means prior to estimate size factors
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
# We normalize and perform the parametric Wald test to determine differentially abundant taxa.
diagdds = DESeq(diagdds, test="Wald", fitType="local")
## using pre-existing size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## -- replacing outliers and refitting for 297 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
## estimating size factors
plotMA(diagdds)
## estimating dispersions
plotDispEsts(diagdds)
# save the results
res = results(diagdds, cooksCutoff = FALSE)
# reorder
res = res[order(res$padj, na.last=NA), ]
#especify contrast
res.PR.sCR <- results(diagdds, contrast=c("Respuesta_100","PR","sCR"))
#Visualizate generated table
res.PR.sCR %>%
kable(format = "html", col.names = colnames(res.PR.sCR )) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "400px")
| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ASV1 | 729.6119002 | -1.4986609 | 1.481991 | -1.0112485 | 0.3118975 | 0.9759373 |
| ASV2 | 647.9485881 | -1.7206118 | 1.752171 | -0.9819884 | 0.3261055 | 0.9919616 |
| ASV3 | 567.9817458 | -1.6673863 | 1.140755 | -1.4616510 | 0.1438369 | 0.8239591 |
| ASV4 | 469.9120778 | -1.8321980 | 1.676678 | -1.0927550 | 0.2745014 | 0.9181599 |
| ASV5 | 335.6251807 | 0.1718067 | 1.840638 | 0.0933409 | 0.9256328 | 0.9919616 |
| ASV6 | 318.8184377 | 0.2142279 | 1.845320 | 0.1160926 | 0.9075792 | 0.9919616 |
| ASV7 | 502.3517870 | -1.6004280 | 1.459003 | -1.0969324 | 0.2726710 | 0.9181599 |
| ASV8 | 231.8964389 | 0.1585241 | 3.285750 | 0.0482459 | 0.9615202 | 0.9919616 |
| ASV9 | 354.9656963 | -1.5880479 | 1.624910 | -0.9773146 | 0.3284134 | 0.9919616 |
| ASV10 | 256.4548414 | 0.2538495 | 1.762246 | 0.1440489 | 0.8854619 | 0.9919616 |
| ASV11 | 220.4540728 | 0.2587368 | 3.285756 | 0.0787450 | 0.9372355 | 0.9919616 |
| ASV12 | 394.4344611 | -1.4672698 | 1.384096 | -1.0600924 | 0.2891026 | 0.9506084 |
| ASV13 | 387.6501461 | 0.1555353 | 1.799128 | 0.0864504 | 0.9311084 | 0.9919616 |
| ASV14 | 324.1069347 | -1.1306624 | 1.612926 | -0.7010006 | 0.4833026 | 0.9919616 |
| ASV15 | 200.5535457 | 0.0647585 | 1.799807 | 0.0359808 | 0.9712977 | 0.9919616 |
| ASV16 | 344.9188676 | -0.5298251 | 1.510569 | -0.3507453 | 0.7257794 | 0.9919616 |
| ASV17 | 98.1964810 | 1.9335166 | 3.286040 | 0.5884032 | 0.5562617 | 0.9919616 |
| ASV18 | 276.9018480 | -1.8157152 | 1.969764 | -0.9217930 | 0.3566366 | 0.9919616 |
| ASV19 | 356.5740209 | 0.3518504 | 1.923800 | 0.1828934 | 0.8548817 | 0.9919616 |
| ASV20 | 287.9218237 | -0.3332220 | 1.845926 | -0.1805175 | 0.8567463 | 0.9919616 |
| ASV21 | 79.4269991 | 2.0999301 | 3.286195 | 0.6390157 | 0.5228127 | 0.9919616 |
| ASV22 | 269.4452622 | 2.7801277 | 3.056320 | 0.9096325 | 0.3630164 | 0.9919616 |
| ASV23 | 278.4514632 | 0.3426924 | 1.952610 | 0.1755047 | 0.8606830 | 0.9919616 |
| ASV24 | 243.9912163 | -0.4321198 | 1.982185 | -0.2180017 | 0.8274278 | 0.9919616 |
| ASV25 | 203.9225727 | 4.3251729 | 3.203453 | 1.3501595 | 0.1769648 | 0.8239591 |
| ASV26 | 212.9596269 | -0.7757047 | 1.414269 | -0.5484845 | 0.5833593 | 0.9919616 |
| ASV27 | 130.9438077 | 0.2653469 | 2.032955 | 0.1305228 | 0.8961528 | 0.9919616 |
| ASV28 | 220.1562885 | 1.1202239 | 3.120900 | 0.3589426 | 0.7196380 | 0.9919616 |
| ASV29 | 77.4021602 | 2.0629353 | 3.286211 | 0.6277551 | 0.5301644 | 0.9919616 |
| ASV30 | 160.9733415 | 0.9049913 | 2.271571 | 0.3983988 | 0.6903363 | 0.9919616 |
| ASV31 | 224.1176959 | 0.2021397 | 1.976725 | 0.1022599 | 0.9185504 | 0.9919616 |
| ASV32 | 205.1326821 | -0.3727775 | 1.886760 | -0.1975755 | 0.8433772 | 0.9919616 |
| ASV33 | 171.2487384 | -1.9942607 | 2.121064 | -0.9402172 | 0.3471061 | 0.9919616 |
| ASV34 | 203.2785965 | 0.8817885 | 3.285741 | 0.2683682 | 0.7884159 | 0.9919616 |
| ASV35 | 170.9246972 | 3.9919722 | 2.560842 | 1.5588516 | NA | NA |
| ASV36 | 161.5216435 | -2.0737110 | 2.122820 | -0.9768663 | 0.3286354 | 0.9919616 |
| ASV37 | 142.5629474 | 0.7749435 | 2.295679 | 0.3375662 | 0.7356901 | 0.9919616 |
| ASV38 | 224.9469085 | -1.7253151 | 2.099813 | -0.8216518 | 0.4112751 | 0.9919616 |
| ASV39 | 76.5044086 | 1.7966353 | 3.175117 | 0.5658484 | 0.5714968 | 0.9919616 |
| ASV40 | 80.0219857 | 2.1775840 | 3.286198 | 0.6626453 | 0.5075577 | 0.9919616 |
| ASV41 | 13.1607033 | 1.2184245 | 3.289774 | 0.3703672 | NA | NA |
| ASV42 | 164.2897896 | -0.1230981 | 1.760244 | -0.0699324 | 0.9442475 | 0.9919616 |
| ASV43 | 96.3928250 | 0.3648341 | 3.286129 | 0.1110225 | 0.9115985 | 0.9919616 |
| ASV44 | 154.1188333 | -0.3650474 | 2.009368 | -0.1816727 | 0.8558396 | 0.9919616 |
| ASV45 | 191.8091766 | -1.2744567 | 1.998382 | -0.6377442 | 0.5236402 | 0.9919616 |
| ASV46 | 74.9889042 | 2.1514907 | 3.286245 | 0.6546957 | 0.5126637 | 0.9919616 |
| ASV47 | 52.8249013 | 0.9780768 | 3.286553 | 0.2975996 | NA | NA |
| ASV48 | 21.0604389 | -21.6189123 | 3.390043 | -6.3771802 | 0.0000000 | 0.0000000 |
| ASV49 | 146.8648450 | 3.0450581 | 2.889235 | 1.0539323 | 0.2919139 | 0.9517866 |
| ASV50 | 136.5104373 | -0.3760083 | 2.153932 | -0.1745683 | 0.8614189 | 0.9919616 |
| ASV51 | 117.6774520 | 0.6283688 | 2.589345 | 0.2426748 | 0.8082573 | 0.9919616 |
| ASV52 | 133.4977730 | -0.0250548 | 2.019118 | -0.0124088 | 0.9900995 | 0.9952295 |
| ASV53 | 147.0733955 | 1.3014031 | 3.285837 | 0.3960644 | 0.6920575 | 0.9919616 |
| ASV54 | 4.6224055 | -5.1142311 | 3.392380 | -1.5075643 | 0.1316661 | 0.8239591 |
| ASV55 | 52.8793226 | 1.1494281 | 3.286533 | 0.3497388 | NA | NA |
| ASV56 | 104.9625328 | -0.3409819 | 2.289277 | -0.1489474 | 0.8815951 | 0.9919616 |
| ASV57 | 108.5927540 | -0.1162813 | 2.274199 | -0.0511307 | 0.9592214 | 0.9919616 |
| ASV58 | 73.4771208 | -2.5146396 | 2.530601 | -0.9936925 | NA | NA |
| ASV59 | 154.7492050 | -1.6283106 | 2.134479 | -0.7628610 | 0.4455463 | 0.9919616 |
| ASV60 | 136.6390867 | -1.1831715 | 2.025412 | -0.5841634 | 0.5591104 | 0.9919616 |
| ASV61 | 20.9664993 | -21.6141366 | 3.390046 | -6.3757658 | 0.0000000 | 0.0000000 |
| ASV62 | 55.9784879 | -1.6604781 | 3.288210 | -0.5049793 | NA | NA |
| ASV63 | 26.8069736 | 25.5388291 | 3.323020 | 7.6854285 | NA | NA |
| ASV64 | 161.7641332 | 1.5093733 | 2.403896 | 0.6278862 | 0.5300785 | 0.9919616 |
| ASV65 | 81.6119418 | 0.1705516 | 2.576779 | 0.0661879 | 0.9472282 | 0.9919616 |
| ASV66 | 105.2490022 | 0.1401011 | 2.433482 | 0.0575723 | 0.9540893 | 0.9919616 |
| ASV67 | 74.9196349 | -2.8076000 | 2.369384 | -1.1849491 | NA | NA |
| ASV68 | 26.8957111 | 5.7690474 | 3.025839 | 1.9065941 | NA | NA |
| ASV69 | 117.3571525 | -0.1675072 | 2.155159 | -0.0777238 | 0.9380477 | 0.9919616 |
| ASV70 | 12.5135518 | 2.4985861 | 3.290537 | 0.7593247 | NA | NA |
| ASV71 | 13.4673147 | 4.1309178 | 3.294256 | 1.2539759 | NA | NA |
| ASV72 | 92.3464960 | 1.0178174 | 2.001669 | 0.5084845 | 0.6111136 | 0.9919616 |
| ASV73 | 101.0965922 | 0.8143772 | 2.287125 | 0.3560702 | 0.7217880 | 0.9919616 |
| ASV74 | 129.7373560 | -0.3492474 | 1.747405 | -0.1998664 | 0.8415851 | 0.9919616 |
| ASV75 | 134.3798926 | -0.4867884 | 1.749176 | -0.2782959 | 0.7807852 | 0.9919616 |
| ASV76 | 122.7898792 | -2.0713988 | 1.992039 | -1.0398386 | 0.2984149 | 0.9568046 |
| ASV77 | 37.8789280 | -0.8495222 | 3.288146 | -0.2583591 | NA | NA |
| ASV78 | 138.2753466 | -24.2427804 | 3.338224 | -7.2621789 | 0.0000000 | 0.0000000 |
| ASV79 | 34.5747689 | 2.0240758 | 3.287141 | 0.6157557 | 0.5380558 | 0.9919616 |
| ASV80 | 117.7473657 | 1.3424219 | 3.285932 | 0.4085361 | 0.6828801 | 0.9919616 |
| ASV81 | 105.9333316 | 0.2802472 | 1.885028 | 0.1486700 | 0.8818140 | 0.9919616 |
| ASV82 | 82.2507713 | 1.2290321 | 2.395312 | 0.5130989 | 0.6078822 | 0.9919616 |
| ASV83 | 46.6562218 | -1.9613577 | 3.289373 | -0.5962709 | NA | NA |
| ASV84 | 93.4199713 | 0.6856970 | 2.585498 | 0.2652089 | 0.7908485 | 0.9919616 |
| ASV85 | 36.6760082 | 1.6387017 | 3.286995 | 0.4985410 | NA | NA |
| ASV86 | 86.0773333 | 0.2100168 | 2.419947 | 0.0867857 | 0.9308419 | 0.9919616 |
| ASV87 | 90.7172497 | -0.8728382 | 2.268956 | -0.3846872 | 0.7004692 | 0.9919616 |
| ASV88 | 32.9963492 | 1.9926359 | 3.287215 | 0.6061776 | 0.5443968 | 0.9919616 |
| ASV89 | 102.9145267 | -2.3340245 | 2.255945 | -1.0346103 | 0.3008509 | 0.9568046 |
| ASV90 | 78.3930056 | 0.7356288 | 1.958344 | 0.3756383 | 0.7071858 | 0.9919616 |
| ASV91 | 111.0160229 | 0.9400974 | 3.285978 | 0.2860937 | NA | NA |
| ASV92 | 123.6839801 | -1.9560215 | 2.421463 | -0.8077851 | 0.4192143 | 0.9919616 |
| ASV93 | 108.1357923 | -1.7595728 | 2.262041 | -0.7778695 | 0.4366460 | 0.9919616 |
| ASV94 | 102.5765207 | 0.5920580 | 2.015561 | 0.2937435 | 0.7689539 | 0.9919616 |
| ASV95 | 18.9632444 | -21.4660581 | 3.390115 | -6.3319549 | 0.0000000 | 0.0000000 |
| ASV96 | 109.5682686 | -0.4250654 | 1.749458 | -0.2429698 | 0.8080288 | 0.9919616 |
| ASV97 | 94.5684637 | 0.1747212 | 1.759452 | 0.0993043 | 0.9208966 | 0.9919616 |
| ASV98 | 130.1495422 | -1.3724365 | 2.281659 | -0.6015083 | 0.5475015 | 0.9919616 |
| ASV99 | 117.7058418 | 1.6244231 | 3.285932 | 0.4943569 | 0.6210542 | 0.9919616 |
| ASV100 | 102.9937411 | -2.1619775 | 1.962247 | -1.1017869 | 0.2705543 | 0.9181599 |
| ASV101 | 37.4555346 | 0.1752158 | 2.825810 | 0.0620055 | NA | NA |
| ASV102 | 30.3563940 | 0.8641839 | 3.236315 | 0.2670272 | 0.7894482 | 0.9919616 |
| ASV103 | 104.7422520 | -0.0628827 | 2.145861 | -0.0293042 | 0.9766220 | 0.9919616 |
| ASV104 | 77.2801867 | 1.0011760 | 1.953669 | 0.5124594 | 0.6083295 | 0.9919616 |
| ASV105 | 112.9382241 | -23.9538555 | 3.303113 | -7.2519033 | 0.0000000 | 0.0000000 |
| ASV106 | 116.4831763 | -3.9291606 | 2.392126 | -1.6425394 | 0.1004783 | 0.8011152 |
| ASV107 | 29.7158086 | -0.7888388 | 3.254288 | -0.2423998 | NA | NA |
| ASV108 | 27.2525697 | 1.3666612 | 3.287526 | 0.4157112 | NA | NA |
| ASV109 | 8.5787506 | 2.9838815 | 3.293954 | 0.9058663 | NA | NA |
| ASV110 | 108.7714319 | -23.9037073 | 3.290335 | -7.2648249 | 0.0000000 | 0.0000000 |
| ASV111 | 50.5524922 | 3.6920541 | 3.287360 | 1.1231060 | NA | NA |
| ASV112 | 89.3307534 | -1.2525755 | 3.199650 | -0.3914726 | 0.6954479 | 0.9919616 |
| ASV113 | 61.5370469 | 1.2055573 | 2.538284 | 0.4749498 | 0.6348228 | 0.9919616 |
| ASV114 | 107.2245667 | -1.3185745 | 2.437291 | -0.5410000 | 0.5885076 | 0.9919616 |
| ASV115 | 83.7157143 | -1.8099497 | 2.096484 | -0.8633262 | 0.3879581 | 0.9919616 |
| ASV116 | 15.0144655 | 24.7446523 | 3.323423 | 7.4455326 | NA | NA |
| ASV117 | 89.4470181 | -0.1933697 | 1.740902 | -0.1110744 | 0.9115573 | 0.9919616 |
| ASV118 | 36.4658140 | 2.0453866 | 3.287059 | 0.6222544 | 0.5337746 | 0.9919616 |
| ASV119 | 96.7864149 | -1.3176077 | 2.586825 | -0.5093533 | 0.6105046 | 0.9919616 |
| ASV120 | 91.3182811 | 0.5654276 | 1.983503 | 0.2850651 | 0.7755942 | 0.9919616 |
| ASV121 | 29.7605845 | 0.5276219 | 2.813538 | 0.1875297 | 0.8512454 | 0.9919616 |
| ASV122 | 59.7716010 | 0.7052857 | 2.086231 | 0.3380669 | 0.7353127 | 0.9919616 |
| ASV123 | 70.0608725 | -0.2748272 | 2.397299 | -0.1146403 | 0.9087302 | 0.9919616 |
| ASV124 | 37.3398965 | 1.9666688 | 3.287006 | 0.5983162 | NA | NA |
| ASV125 | 72.4822695 | 0.0395099 | 3.286442 | 0.0120221 | NA | NA |
| ASV126 | 85.8037812 | 0.8863308 | 3.286138 | 0.2697181 | 0.7873771 | 0.9919616 |
| ASV127 | 81.2073174 | -3.2216932 | 2.207768 | -1.4592536 | 0.1444953 | 0.8239591 |
| ASV128 | 14.0029345 | -21.0527886 | 3.390374 | -6.2095766 | 0.0000000 | 0.0000000 |
| ASV129 | 75.4741929 | 0.5466655 | 2.408316 | 0.2269907 | 0.8204310 | 0.9919616 |
| ASV130 | 84.6584330 | -1.2258763 | 2.825675 | -0.4338349 | 0.6644083 | 0.9919616 |
| ASV131 | 96.1575862 | 1.2092372 | 3.222729 | 0.3752215 | 0.7074957 | 0.9919616 |
| ASV132 | 60.4910383 | -0.1026891 | 2.721032 | -0.0377390 | 0.9698958 | 0.9919616 |
| ASV133 | 77.1898867 | -0.3008719 | 1.761643 | -0.1707904 | 0.8643885 | 0.9919616 |
| ASV134 | 91.4972903 | -1.7801029 | 2.577559 | -0.6906158 | 0.4898070 | 0.9919616 |
| ASV135 | 63.6047328 | 0.7157825 | 1.963856 | 0.3644782 | 0.7155010 | 0.9919616 |
| ASV136 | 57.8012037 | 0.8251714 | 2.565021 | 0.3217017 | 0.7476787 | 0.9919616 |
| ASV137 | 102.0358370 | -2.9711336 | 2.241957 | -1.3252414 | 0.1850911 | 0.8254637 |
| ASV138 | 6.5626060 | 2.0304048 | 3.294337 | 0.6163318 | NA | NA |
| ASV139 | 76.8622643 | -1.5698741 | 2.237371 | -0.7016602 | 0.4828911 | 0.9919616 |
| ASV140 | 6.2522866 | -5.5479189 | 3.391603 | -1.6357809 | 0.1018855 | 0.8011152 |
| ASV141 | 81.6726360 | 0.5822274 | 2.425495 | 0.2400448 | 0.8102955 | 0.9919616 |
| ASV142 | 55.4562266 | 1.1084292 | 2.386938 | 0.4643729 | 0.6423806 | 0.9919616 |
| ASV143 | 90.5791912 | -23.6475421 | 2.798983 | -8.4486197 | 0.0000000 | 0.0000000 |
| ASV144 | 82.3717268 | -1.4105106 | 2.400063 | -0.5876973 | 0.5567355 | 0.9919616 |
| ASV145 | 35.1470250 | 1.7299924 | 3.287069 | 0.5263024 | NA | NA |
| ASV146 | 33.1813171 | 2.1875452 | 3.201299 | 0.6833304 | 0.4943981 | 0.9919616 |
| ASV147 | 76.9935825 | -9.1714264 | 2.113207 | -4.3400514 | 0.0000142 | 0.0003251 |
| ASV148 | 47.4419723 | 0.4534362 | 2.245278 | 0.2019510 | 0.8399550 | 0.9919616 |
| ASV149 | 26.7691252 | -0.3444786 | 2.826824 | -0.1218606 | 0.9030094 | 0.9919616 |
| ASV150 | 68.3408444 | 0.5461074 | 2.555630 | 0.2136880 | 0.8307904 | 0.9919616 |
| ASV151 | 69.3597547 | 0.6442583 | 2.733715 | 0.2356713 | 0.8136877 | 0.9919616 |
| ASV152 | 22.9817179 | 0.7227008 | 3.288079 | 0.2197942 | NA | NA |
| ASV153 | 67.3421817 | -1.4197520 | 2.735684 | -0.5189751 | 0.6037781 | 0.9919616 |
| ASV154 | 33.1585356 | 3.3676116 | 3.287988 | 1.0242165 | NA | NA |
| ASV155 | 58.9457682 | 0.8087817 | 2.405926 | 0.3361623 | 0.7367485 | 0.9919616 |
| ASV156 | 80.0176808 | 2.9913610 | 2.494061 | 1.1993936 | 0.2303749 | 0.8845429 |
| ASV157 | 68.6227101 | 0.0878385 | 2.404225 | 0.0365351 | 0.9708557 | 0.9919616 |
| ASV158 | 62.2145850 | 0.6289519 | 2.238705 | 0.2809445 | 0.7787529 | 0.9919616 |
| ASV159 | 77.5779727 | 1.0698959 | 3.173304 | 0.3371552 | 0.7359999 | 0.9919616 |
| ASV160 | 48.8186867 | -1.4597874 | 2.391080 | -0.6105140 | 0.5415214 | 0.9919616 |
| ASV161 | 23.9642660 | 0.6512551 | 3.288006 | 0.1980699 | NA | NA |
| ASV162 | 17.1081541 | 1.0647190 | 3.288816 | 0.3237394 | NA | NA |
| ASV163 | 12.4978267 | 2.3706809 | 3.290406 | 0.7204829 | 0.4712278 | 0.9919616 |
| ASV164 | 46.7524583 | 0.2694123 | 3.286885 | 0.0819659 | 0.9346739 | 0.9919616 |
| ASV165 | 26.2865534 | -20.7220813 | 3.389912 | -6.1128676 | 0.0000000 | 0.0000000 |
| ASV166 | 57.2151267 | 0.5838973 | 2.096336 | 0.2785324 | 0.7806037 | 0.9919616 |
| ASV167 | 82.8643127 | -2.9766661 | 2.534423 | -1.1744947 | 0.2401969 | 0.8845429 |
| ASV168 | 79.9113593 | -1.1468395 | 2.282712 | -0.5024022 | 0.6153846 | 0.9919616 |
| ASV169 | 30.5030962 | 2.4961217 | 2.952891 | 0.8453145 | 0.3979353 | 0.9919616 |
| ASV170 | 28.6949520 | 2.4676398 | 3.287656 | 0.7505773 | 0.4529071 | 0.9919616 |
| ASV171 | 65.7534356 | -8.9438292 | 2.569298 | -3.4810399 | 0.0004995 | 0.0107664 |
| ASV172 | 65.3240072 | 3.8085358 | 3.287009 | 1.1586630 | 0.2465936 | 0.8941898 |
| ASV173 | 27.8082679 | -21.4318902 | 3.389883 | -6.3223099 | 0.0000000 | 0.0000000 |
| ASV174 | 57.5447541 | 0.6523279 | 2.555788 | 0.2552355 | 0.7985412 | 0.9919616 |
| ASV175 | 57.5698035 | -1.9116103 | 2.226085 | -0.8587318 | 0.3904885 | 0.9919616 |
| ASV176 | 31.2072349 | 2.5723920 | 3.207363 | 0.8020271 | NA | NA |
| ASV177 | 58.1177857 | 0.0114375 | 2.261235 | 0.0050581 | 0.9959643 | 0.9968603 |
| ASV178 | 61.4946545 | 0.2436578 | 2.571961 | 0.0947362 | 0.9245244 | 0.9919616 |
| ASV179 | 50.0383825 | 26.3335235 | 3.322781 | 7.9251453 | NA | NA |
| ASV180 | 19.9949146 | -0.6167675 | 3.025000 | -0.2038901 | NA | NA |
| ASV181 | 21.3556423 | 1.6635071 | 3.288104 | 0.5059168 | 0.6129151 | 0.9919616 |
| ASV182 | 23.8275322 | 1.9593435 | 3.241651 | 0.6044277 | 0.5455594 | 0.9919616 |
| ASV183 | 40.2638844 | -1.9626797 | 2.740299 | -0.7162284 | 0.4738504 | 0.9919616 |
| ASV184 | 53.7619259 | 1.0205699 | 3.286528 | 0.3105313 | 0.7561570 | 0.9919616 |
| ASV185 | 22.3103569 | 3.5452616 | 3.289507 | 1.0777487 | NA | NA |
| ASV186 | 53.4372593 | -0.4119162 | 2.732149 | -0.1507664 | 0.8801600 | 0.9919616 |
| ASV187 | 18.1301853 | 2.8595457 | 3.289325 | 0.8693411 | NA | NA |
| ASV188 | 7.9598140 | -5.8981147 | 3.391125 | -1.7392797 | 0.0819856 | 0.8011152 |
| ASV189 | 53.8165630 | 1.1481840 | 2.550386 | 0.4502001 | 0.6525662 | 0.9919616 |
| ASV190 | 70.8269217 | -1.4271641 | 2.398874 | -0.5949309 | 0.5518896 | 0.9919616 |
| ASV191 | 56.6177617 | -0.5870391 | 2.566645 | -0.2287185 | 0.8190877 | 0.9919616 |
| ASV192 | 46.6467565 | 1.0898651 | 2.397980 | 0.4544930 | 0.6494740 | 0.9919616 |
| ASV193 | 48.7987065 | 4.9216474 | 3.289188 | 1.4963108 | NA | NA |
| ASV194 | 40.7746917 | 1.6752866 | 2.573610 | 0.6509481 | 0.5150800 | 0.9919616 |
| ASV195 | 48.1589724 | 1.2649562 | 3.055738 | 0.4139610 | 0.6789027 | 0.9919616 |
| ASV196 | 4.3287634 | 6.4862999 | 3.325683 | 1.9503662 | NA | NA |
| ASV197 | 54.5010051 | 1.2348629 | 3.286494 | 0.3757386 | 0.7071112 | 0.9919616 |
| ASV198 | 50.8565292 | -22.8484206 | 3.389657 | -6.7406292 | 0.0000000 | 0.0000000 |
| ASV199 | 3.8264294 | 6.2027020 | 3.326226 | 1.8647865 | NA | NA |
| ASV200 | 47.7774942 | 0.3886691 | 3.072326 | 0.1265064 | 0.8993311 | 0.9919616 |
| ASV201 | 44.2030046 | -1.5236868 | 2.560120 | -0.5951623 | 0.5517350 | 0.9919616 |
| ASV202 | 22.6950177 | 0.4795746 | 3.288250 | 0.1458449 | NA | NA |
| ASV203 | 54.1301506 | -1.3265726 | 2.723668 | -0.4870538 | 0.6262202 | 0.9919616 |
| ASV204 | 48.8049748 | 1.8067306 | 3.286622 | 0.5497226 | 0.5825096 | 0.9919616 |
| ASV205 | 55.2765466 | -3.5158315 | 2.488553 | -1.4128016 | 0.1577141 | 0.8239591 |
| ASV206 | 26.3535301 | 2.2435286 | 3.287745 | 0.6823914 | 0.4949915 | 0.9919616 |
| ASV207 | 38.4959885 | 1.0155406 | 3.286956 | 0.3089608 | 0.7573513 | 0.9919616 |
| ASV208 | 14.9229529 | 0.4113114 | 3.255043 | 0.1263613 | NA | NA |
| ASV209 | 11.5976292 | 2.0043164 | 3.290469 | 0.6091279 | NA | NA |
| ASV210 | 18.3863950 | 1.2955764 | 3.254848 | 0.3980452 | NA | NA |
| ASV211 | 29.9647993 | 0.2875438 | 3.025092 | 0.0950529 | 0.9242728 | 0.9919616 |
| ASV212 | 34.0261320 | -1.6277164 | 3.230154 | -0.5039129 | 0.6143226 | 0.9919616 |
| ASV213 | 46.1236192 | 3.1199158 | 3.287113 | 0.9491356 | 0.3425517 | 0.9919616 |
| ASV214 | 15.6301085 | 3.3025154 | 3.290695 | 1.0035921 | NA | NA |
| ASV215 | 46.9193516 | -8.4568881 | 2.448534 | -3.4538574 | 0.0005526 | 0.0112853 |
| ASV216 | 34.4651476 | 0.3815038 | 3.210697 | 0.1188227 | 0.9054158 | 0.9919616 |
| ASV217 | 53.9456636 | -3.4929526 | 2.198408 | -1.5888553 | 0.1120931 | 0.8119757 |
| ASV218 | 5.5393251 | -5.3755306 | 3.391884 | -1.5848214 | 0.1130069 | 0.8119757 |
| ASV219 | 35.0962495 | -0.0550694 | 3.287560 | -0.0167508 | 0.9866354 | 0.9952295 |
| ASV220 | 28.2677789 | 0.4431015 | 3.050036 | 0.1452775 | NA | NA |
| ASV221 | 52.1819645 | 1.1771428 | 3.286545 | 0.3581703 | 0.7202159 | 0.9919616 |
| ASV222 | 22.5137246 | 2.2530047 | 3.216704 | 0.7004078 | 0.4836727 | 0.9919616 |
| ASV223 | 48.3942311 | -0.7778237 | 2.123283 | -0.3663306 | 0.7141184 | 0.9919616 |
| ASV224 | 23.3224540 | -0.5419084 | 3.273306 | -0.1655538 | 0.8685081 | 0.9919616 |
| ASV225 | 17.1820961 | 3.2306344 | 3.290092 | 0.9819283 | 0.3261352 | 0.9919616 |
| ASV226 | 13.9613476 | 1.4794383 | 3.289487 | 0.4497474 | NA | NA |
| ASV227 | 15.4459582 | 0.7451223 | 3.253907 | 0.2289931 | NA | NA |
| ASV228 | 18.0950799 | -21.4099196 | 3.390150 | -6.3153305 | 0.0000000 | 0.0000000 |
| ASV229 | 8.9150563 | 1.2036123 | 3.291834 | 0.3656358 | NA | NA |
| ASV230 | 18.1872632 | 2.2421373 | 3.288772 | 0.6817553 | 0.4953937 | 0.9919616 |
| ASV231 | 36.9628833 | 1.2164316 | 3.286991 | 0.3700745 | 0.7113270 | 0.9919616 |
| ASV232 | 8.4767189 | 1.2217978 | 3.292156 | 0.3711239 | NA | NA |
| ASV233 | 6.3483555 | -5.5716495 | 3.391567 | -1.6427952 | 0.1004253 | 0.8011152 |
| ASV234 | 14.2772380 | 1.1927770 | 3.289440 | 0.3626080 | NA | NA |
| ASV235 | 24.2052086 | 0.7532608 | 3.013809 | 0.2499365 | 0.8026365 | 0.9919616 |
| ASV236 | 9.2810667 | 24.0945809 | 3.323988 | 7.2486960 | NA | NA |
| ASV237 | 36.6586500 | -0.1201238 | 2.605021 | -0.0461124 | 0.9632206 | 0.9919616 |
| ASV238 | 16.3895841 | 2.5984305 | 3.289433 | 0.7899326 | 0.4295671 | 0.9919616 |
| ASV239 | 27.5059995 | 1.9752325 | 3.207687 | 0.6157809 | 0.5380391 | 0.9919616 |
| ASV240 | 41.7755558 | -0.3194602 | 2.588633 | -0.1234088 | 0.9017834 | 0.9919616 |
| ASV241 | 37.3896336 | 0.5503419 | 2.790097 | 0.1972483 | 0.8436332 | 0.9919616 |
| ASV242 | 9.4064437 | 1.0695397 | 3.165097 | 0.3379169 | 0.7354258 | 0.9919616 |
| ASV243 | 21.2411784 | 2.5452159 | 3.288484 | 0.7739784 | 0.4389435 | 0.9919616 |
| ASV244 | 49.5806522 | 0.0121312 | 3.082893 | 0.0039350 | 0.9968603 | 0.9968603 |
| ASV245 | 10.6788339 | 0.6902524 | 3.291137 | 0.2097306 | NA | NA |
| ASV246 | 13.0591888 | 0.0089209 | 3.290996 | 0.0027107 | NA | NA |
| ASV247 | 9.9327054 | -6.2175365 | 3.390779 | -1.8336600 | 0.0667045 | 0.8011152 |
| ASV248 | 32.5375699 | 3.7352951 | 3.288473 | 1.1358753 | NA | NA |
| ASV249 | 35.4814086 | -0.8575129 | 2.796340 | -0.3066554 | 0.7591057 | 0.9919616 |
| ASV250 | 52.1309124 | 0.8530368 | 3.286586 | 0.2595510 | 0.7952101 | 0.9919616 |
| ASV251 | 19.5520259 | 2.0659194 | 3.288457 | 0.6282338 | 0.5298508 | 0.9919616 |
| ASV252 | 39.2518547 | -0.5221785 | 2.986130 | -0.1748679 | 0.8611834 | 0.9919616 |
| ASV253 | 43.6216423 | 2.6592966 | 3.286972 | 0.8090414 | 0.4184913 | 0.9919616 |
| ASV254 | 25.4158867 | 1.2303131 | 3.021052 | 0.4072465 | 0.6838269 | 0.9919616 |
| ASV255 | 12.4987439 | 24.4982886 | 3.323607 | 7.3709940 | NA | NA |
| ASV256 | 24.2134821 | -1.5551125 | 3.010744 | -0.5165209 | 0.6054906 | 0.9919616 |
| ASV257 | 39.0395996 | -1.9672033 | 3.290153 | -0.5979064 | 0.5499024 | 0.9919616 |
| ASV258 | 27.0924502 | 0.6565059 | 2.833005 | 0.2317348 | 0.8167440 | 0.9919616 |
| ASV259 | 35.6569182 | -8.0610652 | 2.652187 | -3.0394032 | 0.0023705 | 0.0459872 |
| ASV260 | 16.9292489 | 0.0401853 | 3.289687 | 0.0122155 | NA | NA |
| ASV261 | 19.3507345 | 1.7627943 | 3.288394 | 0.5360654 | NA | NA |
| ASV262 | 34.2345214 | 1.4738692 | 3.190006 | 0.4620270 | 0.6440619 | 0.9919616 |
| ASV263 | 29.8613336 | 0.6897162 | 3.287487 | 0.2098004 | 0.8338234 | 0.9919616 |
| ASV264 | 32.6662047 | -2.1561104 | 3.201494 | -0.6734701 | 0.5006482 | 0.9919616 |
| ASV265 | 7.6861822 | 23.8374323 | 3.324296 | 7.1706707 | NA | NA |
| ASV266 | 14.8354575 | 0.7898259 | 3.289476 | 0.2401069 | 0.8102474 | 0.9919616 |
| ASV267 | 31.9668768 | -0.5587962 | 3.288257 | -0.1699369 | 0.8650598 | 0.9919616 |
| ASV268 | 31.9146218 | -0.8801095 | 3.238485 | -0.2717658 | 0.7858021 | 0.9919616 |
| ASV269 | 11.4759410 | 4.5683018 | 3.298466 | 1.3849778 | 0.1660593 | 0.8239591 |
| ASV270 | 20.4304992 | 1.6167745 | 3.288219 | 0.4916870 | 0.6229407 | 0.9919616 |
| ASV271 | 4.6138978 | -5.1113368 | 3.392386 | -1.5067084 | 0.1318854 | 0.8239591 |
| ASV272 | 7.5106329 | 1.2960818 | 3.292987 | 0.3935885 | NA | NA |
| ASV273 | 20.1399236 | 1.9597788 | 3.288329 | 0.5959802 | 0.5511885 | 0.9919616 |
| ASV274 | 25.6686758 | -0.2381852 | 3.260728 | -0.0730466 | 0.9417690 | 0.9919616 |
| ASV275 | 21.9567158 | 1.6583223 | 3.288030 | 0.5043513 | 0.6140145 | 0.9919616 |
| ASV276 | 21.5756826 | -21.6536144 | 3.390027 | -6.3874459 | 0.0000000 | 0.0000000 |
| ASV277 | 34.6437893 | 2.3887362 | 2.953981 | 0.8086498 | 0.4187166 | 0.9919616 |
| ASV278 | 45.0953610 | -1.0949873 | 2.758066 | -0.3970127 | 0.6913581 | 0.9919616 |
| ASV279 | 39.5523689 | -0.7212364 | 3.287885 | -0.2193618 | 0.8263682 | 0.9919616 |
| ASV280 | 31.4389091 | 0.7801898 | 3.235443 | 0.2411385 | NA | NA |
| ASV281 | 16.6110098 | 1.7512755 | 3.288877 | 0.5324843 | 0.5943906 | 0.9919616 |
| ASV282 | 30.2849348 | 0.9869484 | 3.287371 | 0.3002243 | 0.7640061 | 0.9919616 |
| ASV283 | 7.4610846 | 1.0107439 | 3.293197 | 0.3069187 | 0.7589052 | 0.9919616 |
| ASV284 | 22.4614687 | 0.6796489 | 3.256092 | 0.2087315 | 0.8346578 | 0.9919616 |
| ASV285 | 10.2558500 | -0.4690832 | 3.293867 | -0.1424111 | NA | NA |
| ASV286 | 33.6273193 | -0.0593059 | 3.235164 | -0.0183316 | 0.9853743 | 0.9952295 |
| ASV287 | 10.9558645 | 3.4559180 | 3.293399 | 1.0493469 | NA | NA |
| ASV288 | 23.1469912 | -0.9516819 | 3.252442 | -0.2926054 | 0.7698238 | 0.9919616 |
| ASV289 | 33.7411213 | 0.5850212 | 3.287291 | 0.1779645 | 0.8587509 | 0.9919616 |
| ASV290 | 5.0439992 | -5.2372063 | 3.392136 | -1.5439260 | 0.1226063 | 0.8239591 |
| ASV291 | 17.7192392 | 0.2909657 | 3.289209 | 0.0884607 | 0.9295105 | 0.9919616 |
| ASV292 | 16.3203729 | 1.6685669 | 3.288923 | 0.5073293 | 0.6119238 | 0.9919616 |
| ASV293 | 21.7574716 | -0.5882902 | 3.263430 | -0.1802674 | 0.8569426 | 0.9919616 |
| ASV294 | 16.4221681 | 2.7611878 | 3.289603 | 0.8393681 | 0.4012628 | 0.9919616 |
| ASV295 | 6.0180935 | -5.4934464 | 3.391688 | -1.6196791 | 0.1053012 | 0.8011152 |
| ASV296 | 7.8064369 | 3.9845315 | 3.299557 | 1.2075958 | NA | NA |
| ASV297 | 9.9510813 | 24.1891958 | 3.323889 | 7.2773777 | NA | NA |
| ASV298 | 1.4781120 | 4.9375253 | 3.331789 | 1.4819440 | NA | NA |
| ASV299 | 14.9884137 | -21.1466952 | 3.369857 | -6.2752507 | 0.0000000 | 0.0000000 |
| ASV300 | 31.0897454 | 1.4297142 | 3.287267 | 0.4349249 | 0.6636170 | 0.9919616 |
| ASV301 | 7.9523599 | 23.8837630 | 3.324237 | 7.1847365 | NA | NA |
| ASV302 | 18.2433368 | -0.8310908 | 3.290990 | -0.2525352 | 0.8006274 | 0.9919616 |
| ASV303 | 2.0995546 | -3.9849366 | 3.395931 | -1.1734444 | 0.2406176 | 0.8845429 |
| ASV304 | 23.9470950 | 1.2435789 | 3.287824 | 0.3782377 | NA | NA |
| ASV305 | 19.4090722 | 2.0256975 | 3.288461 | 0.6160018 | 0.5378934 | 0.9919616 |
| ASV306 | 6.8574877 | 1.2261718 | 3.293736 | 0.3722739 | NA | NA |
| ASV307 | 25.5499679 | 1.5054092 | 3.025120 | 0.4976362 | 0.6187405 | 0.9919616 |
| ASV308 | 15.3383733 | 0.1406839 | 2.996615 | 0.0469476 | 0.9625550 | 0.9919616 |
| ASV309 | 20.5325467 | -4.2107017 | 2.789221 | -1.5096336 | 0.1311369 | 0.8239591 |
| ASV310 | 12.6632098 | -0.2869019 | 2.970557 | -0.0965818 | NA | NA |
| ASV311 | 6.2437627 | -5.5469686 | 3.391604 | -1.6354999 | 0.1019443 | 0.8011152 |
| ASV312 | 10.0826985 | 1.8543329 | 3.291134 | 0.5634328 | NA | NA |
| ASV313 | 22.3408421 | -0.0531274 | 3.288762 | -0.0161542 | NA | NA |
| ASV314 | 7.8861252 | 2.6442201 | 3.293810 | 0.8027847 | NA | NA |
| ASV315 | 15.1683373 | 1.9318736 | 3.289260 | 0.5873277 | NA | NA |
| ASV316 | 13.3758267 | 3.6666508 | 3.292579 | 1.1136104 | 0.2654464 | 0.9179515 |
| ASV317 | 8.4905960 | 1.2789185 | 3.292129 | 0.3884777 | NA | NA |
| ASV318 | 30.7943441 | 0.6231298 | 3.239203 | 0.1923714 | 0.8474513 | 0.9919616 |
| ASV319 | 28.2112952 | -0.2694610 | 2.848642 | -0.0945928 | NA | NA |
| ASV320 | 18.4037310 | 2.0780666 | 3.288649 | 0.6318907 | NA | NA |
| ASV321 | 22.5055690 | 2.6995797 | 3.288432 | 0.8209323 | 0.4116848 | 0.9919616 |
| ASV322 | 14.8047208 | -2.2036178 | 3.244258 | -0.6792363 | 0.4969881 | 0.9919616 |
| ASV323 | 9.4589579 | 0.8903929 | 3.291664 | 0.2704993 | NA | NA |
| ASV324 | 21.0513109 | 1.6556005 | 3.229021 | 0.5127252 | 0.6081435 | 0.9919616 |
| ASV325 | 6.4577070 | 1.6873393 | 3.294222 | 0.5122118 | NA | NA |
| ASV326 | 22.5854420 | 0.0478903 | 3.252817 | 0.0147227 | 0.9882534 | 0.9952295 |
| ASV327 | 19.9823089 | -2.8710827 | 2.984753 | -0.9619164 | 0.3360916 | 0.9919616 |
| ASV328 | 19.2104796 | -1.3709899 | 2.624961 | -0.5222896 | 0.6014687 | 0.9919616 |
| ASV329 | 5.0530925 | 6.7096768 | 3.325228 | 2.0178098 | 0.0436111 | 0.7356998 |
| ASV330 | 25.7234206 | -21.8979274 | 3.388500 | -6.4624244 | 0.0000000 | 0.0000000 |
| ASV331 | 7.2275408 | 4.6623864 | 3.307107 | 1.4098081 | 0.1585964 | 0.8239591 |
| ASV332 | 15.4107524 | 1.7090247 | 3.289137 | 0.5195966 | 0.6033448 | 0.9919616 |
| ASV333 | 9.8369510 | 2.2011020 | 3.291550 | 0.6687129 | NA | NA |
| ASV334 | 31.8099597 | -22.1947664 | 3.351546 | -6.6222471 | 0.0000000 | 0.0000000 |
| ASV335 | 27.1051847 | 2.0826697 | 3.229479 | 0.6448934 | 0.5189963 | 0.9919616 |
| ASV336 | 6.5372984 | -5.6122495 | 3.391506 | -1.6547956 | 0.0979660 | 0.8011152 |
| ASV337 | 2.5298948 | -4.2431824 | 3.394860 | -1.2498844 | 0.2113418 | 0.8814503 |
| ASV338 | 9.6617263 | 3.5721724 | 3.294911 | 1.0841482 | 0.2782991 | 0.9229064 |
| ASV339 | 25.5256802 | 0.0969723 | 3.288212 | 0.0294909 | 0.9764731 | 0.9919616 |
| ASV340 | 3.2319751 | 6.0645677 | 3.326761 | 1.8229647 | 0.0683087 | 0.8011152 |
| ASV341 | 22.3250017 | 1.1194853 | 3.256602 | 0.3437587 | 0.7310277 | 0.9919616 |
| ASV342 | 6.8223288 | 0.7725510 | 3.294216 | 0.2345174 | NA | NA |
| ASV343 | 2.2755830 | 5.5601056 | 3.328540 | 1.6704339 | NA | NA |
| ASV344 | 11.7327809 | 0.5186811 | 3.290811 | 0.1576150 | 0.8747602 | 0.9919616 |
| ASV345 | 10.3763412 | 24.2051116 | 3.323832 | 7.2822900 | NA | NA |
| ASV346 | 13.5946642 | 0.5370045 | 3.290062 | 0.1632202 | 0.8703451 | 0.9919616 |
| ASV347 | 3.5719895 | 22.7924678 | 3.326353 | 6.8520890 | NA | NA |
| ASV348 | 10.9576087 | 1.1924889 | 3.290646 | 0.3623875 | 0.7170625 | 0.9919616 |
| ASV349 | 12.6577918 | 2.3155956 | 3.290286 | 0.7037673 | 0.4815777 | 0.9919616 |
| ASV350 | 25.2809479 | 0.5876692 | 3.261714 | 0.1801719 | 0.8570176 | 0.9919616 |
| ASV351 | 1.0759840 | -3.0155157 | 3.402181 | -0.8863478 | 0.3754301 | 0.9919616 |
| ASV352 | 2.5994486 | -4.2911586 | 3.394681 | -1.2640830 | 0.2062002 | 0.8791834 |
| ASV353 | 8.4659340 | -0.2966824 | 3.294978 | -0.0900408 | NA | NA |
| ASV354 | 6.6175349 | -5.6308267 | 3.391479 | -1.6602865 | 0.0968568 | 0.8011152 |
| ASV355 | 9.6891171 | 0.5318415 | 3.291923 | 0.1615595 | NA | NA |
| ASV356 | 16.1466007 | 2.4615437 | 3.289362 | 0.7483348 | NA | NA |
| ASV357 | 1.1744862 | 1.4962560 | 3.333397 | 0.4488682 | 0.6535267 | 0.9919616 |
| ASV358 | 4.5719080 | -5.0969026 | 3.392416 | -1.5024402 | 0.1329835 | 0.8239591 |
| ASV359 | 4.1099175 | -4.9429479 | 3.392757 | -1.4569117 | 0.1451408 | 0.8239591 |
| ASV360 | 9.1161164 | 0.2616687 | 3.292802 | 0.0794669 | NA | NA |
| ASV361 | 2.9150425 | 5.9160224 | 3.327222 | 1.7780667 | NA | NA |
| ASV362 | 10.5798567 | 2.2664968 | 3.291188 | 0.6886562 | 0.4910397 | 0.9919616 |
| ASV363 | 8.1041759 | 3.5559606 | 2.750697 | 1.2927492 | 0.1960978 | 0.8646131 |
| ASV364 | 4.3866723 | -5.0390297 | 3.392540 | -1.4853264 | 0.1374574 | 0.8239591 |
| ASV365 | 17.5116212 | 2.8908571 | 3.289501 | 0.8788132 | NA | NA |
| ASV366 | 11.1577084 | -0.1605420 | 3.292323 | -0.0487625 | NA | NA |
| ASV367 | 15.7930769 | 0.1635221 | 2.781788 | 0.0587831 | 0.9531249 | 0.9919616 |
| ASV368 | 22.6282192 | 2.7141937 | 3.288428 | 0.8253773 | 0.4091574 | 0.9919616 |
| ASV369 | 5.7247150 | 1.3677967 | 3.295281 | 0.4150774 | NA | NA |
| ASV370 | 1.1381014 | -3.0806698 | 3.401618 | -0.9056484 | 0.3651220 | 0.9919616 |
| ASV371 | 5.1695403 | 1.9520955 | 3.296663 | 0.5921428 | NA | NA |
| ASV372 | 4.4594402 | 23.0867014 | 3.325592 | 6.9421330 | NA | NA |
| ASV373 | 2.1918493 | -4.0429070 | 3.395674 | -1.1906053 | 0.2338086 | 0.8845429 |
| ASV374 | 15.9080229 | 1.6743958 | 3.289014 | 0.5090875 | 0.6106909 | 0.9919616 |
| ASV375 | 2.3550960 | -4.1492944 | 3.395227 | -1.2220962 | 0.2216713 | 0.8845429 |
| ASV376 | 6.9135138 | -5.6946465 | 3.391388 | -1.6791490 | 0.0931230 | 0.8011152 |
| ASV377 | 8.1874175 | 1.4892460 | 3.292338 | 0.4523369 | NA | NA |
| ASV378 | 2.4720810 | 5.6770332 | 3.328070 | 1.7058032 | NA | NA |
| ASV379 | 9.7603254 | 3.7740186 | 3.295689 | 1.1451381 | NA | NA |
| ASV380 | 3.7848130 | 6.2923456 | 3.326140 | 1.8917862 | NA | NA |
| ASV381 | 8.7680420 | 2.1490889 | 3.292217 | 0.6527786 | NA | NA |
| ASV382 | 9.7940068 | 3.4540112 | 3.294331 | 1.0484712 | NA | NA |
| ASV383 | 23.3290658 | -7.4488281 | 3.143582 | -2.3695349 | 0.0178105 | 0.3290697 |
| ASV384 | 8.8949448 | 1.6494805 | 3.291812 | 0.5010859 | NA | NA |
| ASV385 | 3.1147029 | 6.0115655 | 3.326920 | 1.8069463 | NA | NA |
| ASV386 | 6.6812132 | -5.6445462 | 3.391459 | -1.6643415 | 0.0960442 | 0.8011152 |
| ASV387 | 6.4190207 | 0.1818046 | 3.296135 | 0.0551569 | NA | NA |
| ASV388 | 6.4079418 | -5.5823730 | 3.391551 | -1.6459649 | 0.0997710 | 0.8011152 |
| ASV389 | 11.3484217 | 2.7554524 | 3.291442 | 0.8371566 | 0.4025046 | 0.9919616 |
| ASV390 | 7.0834173 | 1.6255554 | 3.293411 | 0.4935780 | 0.6216042 | 0.9919616 |
| ASV391 | 6.8358996 | -5.6774673 | 3.391412 | -1.6740716 | 0.0941165 | 0.8011152 |
| ASV392 | 3.1347386 | -4.5518680 | 3.393806 | -1.3412279 | 0.1798465 | 0.8239591 |
| ASV393 | 10.2962006 | -6.2675505 | 3.390732 | -1.8484359 | 0.0645393 | 0.8011152 |
| ASV394 | 3.4740917 | 6.1690542 | 3.326464 | 1.8545382 | NA | NA |
| ASV395 | 10.2387843 | 0.5422407 | 3.291565 | 0.1647364 | NA | NA |
| ASV396 | 23.2932545 | 1.2940604 | 3.287885 | 0.3935845 | 0.6938879 | 0.9919616 |
| ASV397 | 4.3745643 | 2.4271239 | 3.299739 | 0.7355502 | NA | NA |
| ASV398 | 4.7484277 | 5.4350098 | 3.324544 | 1.6348136 | NA | NA |
| ASV399 | 3.5911397 | -4.7489913 | 3.393242 | -1.3995438 | 0.1616500 | 0.8239591 |
| ASV400 | 13.8403938 | 1.3775190 | 3.289532 | 0.4187583 | 0.6753928 | 0.9919616 |
| ASV401 | 7.1472598 | 1.7943223 | 3.293413 | 0.5448216 | NA | NA |
| ASV402 | 6.0998737 | -5.5128067 | 3.391657 | -1.6254020 | 0.1040769 | 0.8011152 |
| ASV403 | 7.1915050 | -1.5644982 | 3.167674 | -0.4938950 | NA | NA |
| ASV404 | 14.0543729 | 0.9313149 | 3.289614 | 0.2831077 | 0.7770943 | 0.9919616 |
| ASV405 | 13.8193594 | -6.6931086 | 3.358081 | -1.9931350 | 0.0462467 | 0.7476544 |
| ASV406 | 3.3270091 | -1.4441478 | 3.326308 | -0.4341594 | 0.6641727 | 0.9919616 |
| ASV407 | 9.7024443 | 1.6426026 | 3.291280 | 0.4990771 | 0.6177251 | 0.9919616 |
| ASV408 | 11.7020946 | -0.6230667 | 3.293328 | -0.1891906 | 0.8499434 | 0.9919616 |
| ASV409 | 8.9737252 | 1.9195733 | 3.291875 | 0.5831247 | 0.5598094 | 0.9919616 |
| ASV410 | 2.7476842 | 5.8320359 | 3.327504 | 1.7526758 | NA | NA |
| ASV411 | 9.7297698 | 0.1064829 | 3.292665 | 0.0323394 | 0.9742014 | 0.9919616 |
| ASV412 | 6.9994500 | -5.7105018 | 3.391367 | -1.6838351 | 0.0922135 | 0.8011152 |
| ASV413 | 0.0000000 | 0.0000000 | 0.000000 | 0.0000000 | 1.0000000 | NA |
| ASV414 | 4.4228487 | 1.0500917 | 3.298443 | 0.3183598 | NA | NA |
| ASV415 | 2.5690254 | 5.7325532 | 3.327861 | 1.7225941 | NA | NA |
| ASV416 | 12.3453881 | -0.6414907 | 3.292975 | -0.1948058 | 0.8455449 | 0.9919616 |
| ASV417 | 6.6150192 | -5.6323120 | 3.391477 | -1.6607255 | 0.0967686 | 0.8011152 |
| ASV418 | 3.8036444 | -4.8298986 | 3.393032 | -1.4234757 | 0.1545983 | 0.8239591 |
| ASV419 | 16.0023439 | 2.6099305 | 3.289538 | 0.7934033 | 0.4275429 | 0.9919616 |
| ASV420 | 2.4239814 | 5.6495918 | 3.328177 | 1.6975033 | NA | NA |
| ASV421 | 2.3881343 | -4.1661621 | 3.395160 | -1.2270887 | 0.2197892 | 0.8845429 |
| ASV422 | 6.7740307 | -5.6656290 | 3.391429 | -1.6705727 | 0.0948061 | 0.8011152 |
| ASV423 | 7.6803861 | -5.8441031 | 3.391191 | -1.7233186 | 0.0848309 | 0.8011152 |
| ASV424 | 4.3557170 | 0.9889644 | 3.298775 | 0.2997975 | 0.7643316 | 0.9919616 |
| ASV425 | 5.7186088 | 6.8864402 | 3.324914 | 2.0711635 | 0.0383435 | 0.6762403 |
| ASV426 | 8.5202847 | 3.6565795 | 3.296575 | 1.1092055 | 0.2673415 | 0.9179515 |
| ASV427 | 2.5642318 | 0.3011807 | 3.311220 | 0.0909576 | NA | NA |
| ASV428 | 8.2709097 | -5.9519622 | 3.391061 | -1.7551916 | 0.0792265 | 0.8011152 |
| ASV429 | 5.3842392 | 2.7507337 | 3.298058 | 0.8340464 | NA | NA |
| ASV430 | 3.3595531 | 6.1204137 | 3.326599 | 1.8398409 | NA | NA |
| ASV431 | 13.5419296 | 2.0505148 | 3.289780 | 0.6232985 | 0.5330884 | 0.9919616 |
| ASV432 | 2.1745387 | -4.0352213 | 3.395707 | -1.1883301 | 0.2347034 | 0.8845429 |
| ASV433 | 3.4265072 | -4.6755238 | 3.393444 | -1.3778110 | 0.1682617 | 0.8239591 |
| ASV434 | 2.1011621 | -3.9762197 | 3.395971 | -1.1708639 | 0.2416535 | 0.8845429 |
| ASV435 | 1.9410121 | -3.8718021 | 3.396464 | -1.1399508 | 0.2543068 | 0.9136207 |
| ASV436 | 6.1651223 | 4.6268684 | 3.310374 | 1.3976874 | NA | NA |
| ASV437 | 7.9489448 | 0.7476275 | 3.293017 | 0.2270342 | 0.8203971 | 0.9919616 |
| ASV438 | 3.9901374 | 2.2086291 | 3.300447 | 0.6691909 | NA | NA |
| ASV439 | 1.4523470 | -3.4419637 | 3.398915 | -1.0126654 | 0.3112200 | 0.9759373 |
| ASV440 | 1.1979627 | 4.6338383 | 3.333960 | 1.3898904 | NA | NA |
| ASV441 | 3.0751319 | -4.5297205 | 3.393875 | -1.3346752 | 0.1819827 | 0.8239591 |
| ASV442 | 7.7704493 | -0.5019681 | 3.296708 | -0.1522634 | 0.8789792 | 0.9919616 |
| ASV443 | 3.1325607 | 6.0167761 | 3.326904 | 1.8085211 | NA | NA |
| ASV444 | 7.8490268 | 1.7870771 | 3.292708 | 0.5427377 | NA | NA |
| ASV445 | 0.4196783 | -1.7327632 | 3.419279 | -0.5067628 | 0.6123213 | 0.9919616 |
| ASV446 | 11.1654858 | -6.3848205 | 3.390627 | -1.8830798 | 0.0596896 | 0.8011152 |
| ASV447 | 6.7424884 | 4.1324456 | 3.303034 | 1.2511060 | NA | NA |
| ASV448 | 3.3273285 | -4.6401090 | 3.393544 | -1.3673341 | 0.1715206 | 0.8239591 |
| ASV449 | 3.7199159 | 6.2651016 | 3.326209 | 1.8835562 | NA | NA |
| ASV450 | 4.5030560 | 6.5411053 | 3.325565 | 1.9669157 | NA | NA |
| ASV451 | 2.2228125 | -4.0566065 | 3.395614 | -1.1946605 | 0.2322197 | 0.8845429 |
| ASV452 | 7.1174911 | 1.0877348 | 3.293514 | 0.3302657 | NA | NA |
| ASV453 | 8.1716461 | 2.9002726 | 3.294150 | 0.8804314 | 0.3786257 | 0.9919616 |
| ASV454 | 10.3049731 | 0.8196009 | 3.291217 | 0.2490267 | NA | NA |
| ASV455 | 7.3939638 | 3.2358032 | 3.296264 | 0.9816578 | NA | NA |
| ASV456 | 0.7229418 | 3.9046857 | 3.341463 | 1.1685556 | NA | NA |
| ASV457 | 5.3749674 | 2.4530743 | 3.297154 | 0.7439975 | NA | NA |
| ASV458 | 3.9974429 | 22.8884702 | 3.325947 | 6.8817911 | NA | NA |
| ASV459 | 2.6255649 | 5.7665333 | 3.327736 | 1.7328698 | NA | NA |
| ASV460 | 3.8859625 | -4.8643999 | 3.392946 | -1.4336803 | 0.1516635 | 0.8239591 |
| ASV461 | 4.3263698 | -5.0178655 | 3.392587 | -1.4790676 | 0.1391222 | 0.8239591 |
| ASV462 | 2.2113514 | 5.5171646 | 3.328722 | 1.6574424 | NA | NA |
| ASV463 | 5.6843428 | -5.4116591 | 3.391822 | -1.5955019 | 0.1106000 | 0.8119757 |
| ASV464 | 15.0021654 | 1.3422671 | 3.289220 | 0.4080806 | 0.6832145 | 0.9919616 |
| ASV465 | 7.9773806 | 1.2336232 | 3.292576 | 0.3746681 | NA | NA |
| ASV466 | 1.8739961 | -3.8099752 | 3.396774 | -1.1216453 | 0.2620133 | 0.9179515 |
| ASV467 | 11.8041325 | 2.4503960 | 3.290782 | 0.7446244 | 0.4564988 | 0.9919616 |
| ASV468 | 4.0685959 | -4.9270168 | 3.392795 | -1.4522000 | 0.1464460 | 0.8239591 |
| ASV469 | 8.8361550 | -6.0470282 | 3.390954 | -1.7832821 | 0.0745404 | 0.8011152 |
| ASV470 | 3.2745011 | 6.0834242 | 3.326706 | 1.8286632 | NA | NA |
| ASV471 | 12.8718050 | 1.8700006 | 3.289916 | 0.5684038 | 0.5697608 | 0.9919616 |
| ASV472 | 5.1334321 | -5.2625545 | 3.392088 | -1.5514205 | 0.1208009 | 0.8239591 |
| ASV473 | 4.7372088 | 3.9529295 | 3.308363 | 1.1948294 | NA | NA |
| ASV474 | 1.3573802 | 1.4857576 | 3.326681 | 0.4466186 | NA | NA |
| ASV475 | 0.2235279 | -0.9057717 | 3.424764 | -0.2644771 | 0.7914123 | 0.9919616 |
| ASV476 | 3.0699998 | -4.5230462 | 3.393896 | -1.3327005 | 0.1826301 | 0.8239591 |
| ASV477 | 2.4847749 | -4.2228624 | 3.394937 | -1.2438705 | 0.2135472 | 0.8814503 |
| ASV478 | 10.2826705 | 1.3143547 | 3.290955 | 0.3993840 | NA | NA |
| ASV479 | 6.6547053 | 1.9096359 | 3.294104 | 0.5797134 | NA | NA |
| ASV480 | 10.4738435 | 1.7826559 | 3.290896 | 0.5416932 | NA | NA |
| ASV481 | 0.2249523 | -1.1882658 | 3.423912 | -0.3470492 | 0.7285544 | 0.9919616 |
| ASV482 | 2.5087220 | -4.2308116 | 3.394907 | -1.2462232 | 0.2126825 | 0.8814503 |
| ASV483 | 3.6658572 | -4.7790901 | 3.393163 | -1.4084471 | 0.1589987 | 0.8239591 |
| ASV484 | 11.2542792 | 2.5356928 | 3.291159 | 0.7704558 | 0.4410296 | 0.9919616 |
| ASV485 | 6.1345981 | 23.5259778 | 3.324751 | 7.0760117 | NA | NA |
| ASV486 | 9.2624884 | -6.1148235 | 3.390882 | -1.8033134 | 0.0713390 | 0.8011152 |
| ASV487 | 0.8368765 | -2.6543018 | 3.405805 | -0.7793463 | 0.4357757 | 0.9919616 |
| ASV488 | 4.9177500 | -5.1987673 | 3.392210 | -1.5325607 | 0.1253841 | 0.8239591 |
| ASV489 | 8.1782436 | 0.2400018 | 3.293691 | 0.0728671 | 0.9419118 | 0.9919616 |
| ASV490 | 1.7402000 | 5.1741333 | 3.330387 | 1.5536130 | NA | NA |
| ASV491 | 5.8695864 | 1.1652273 | 3.295163 | 0.3536175 | NA | NA |
| ASV492 | 4.7635678 | 4.0566959 | 3.309351 | 1.2258284 | NA | NA |
| ASV493 | 12.1468607 | 0.9961102 | 3.290228 | 0.3027481 | 0.7620818 | 0.9919616 |
| ASV494 | 3.3296712 | 6.1075470 | 3.326636 | 1.8359529 | 0.0663646 | 0.8011152 |
| ASV495 | 5.5991049 | 3.3507677 | 3.300292 | 1.0152943 | NA | NA |
| ASV496 | 8.3510393 | 2.2072796 | 3.292637 | 0.6703683 | 0.5026230 | 0.9919616 |
| ASV497 | 11.3134399 | 5.4331437 | 3.307194 | 1.6428258 | 0.1004190 | 0.8011152 |
| ASV498 | 3.3568899 | -4.6533701 | 3.393506 | -1.3712572 | 0.1702948 | 0.8239591 |
| ASV499 | 7.8805435 | 2.4221323 | 3.293401 | 0.7354501 | NA | NA |
| ASV500 | 2.0580684 | 3.6708641 | 3.327078 | 1.1033296 | NA | NA |
| ASV501 | 0.1108584 | 1.3643242 | 3.418344 | 0.3991184 | 0.6898059 | 0.9919616 |
| ASV502 | 3.3367237 | 2.3049513 | 3.303658 | 0.6976967 | NA | NA |
| ASV503 | 2.3020737 | -4.1059955 | 3.395405 | -1.2092800 | 0.2265553 | 0.8845429 |
| ASV504 | 2.1998060 | 2.3223581 | 3.313194 | 0.7009424 | NA | NA |
| ASV505 | 1.5662804 | 5.0142549 | 3.331309 | 1.5051904 | NA | NA |
| ASV506 | 4.6581141 | 0.8176914 | 3.298194 | 0.2479210 | NA | NA |
| ASV507 | 2.8067152 | 5.8610626 | 3.327405 | 1.7614517 | NA | NA |
| ASV508 | 1.8814542 | 5.2848882 | 3.329806 | 1.5871460 | NA | NA |
| ASV509 | 4.7639619 | -5.1581567 | 3.392290 | -1.5205528 | 0.1283721 | 0.8239591 |
| ASV510 | 0.3779434 | -1.5609347 | 3.421622 | -0.4561973 | 0.6482481 | 0.9919616 |
| ASV511 | 0.6786105 | 3.8101904 | 3.342741 | 1.1398400 | NA | NA |
| ASV512 | 3.4953751 | -4.7085942 | 3.393352 | -1.3875939 | 0.1652607 | 0.8239591 |
| ASV513 | 2.1844963 | 1.0296233 | 3.311992 | 0.3108773 | NA | NA |
| ASV514 | 2.2897368 | 5.5693694 | 3.328501 | 1.6732365 | NA | NA |
| ASV515 | 3.2428620 | -4.6014779 | 3.393657 | -1.3559053 | 0.1751293 | 0.8239591 |
| ASV516 | 0.7765836 | 0.4665551 | 3.365866 | 0.1386137 | 0.8897554 | 0.9919616 |
| ASV517 | 3.1752097 | 0.0522770 | 3.307868 | 0.0158038 | NA | NA |
| ASV518 | 9.8187479 | 3.9116049 | 3.296317 | 1.1866591 | 0.2353621 | 0.8845429 |
| ASV519 | 1.3976231 | 4.8560517 | 3.332328 | 1.4572552 | NA | NA |
| ASV520 | 2.4468189 | 1.8987541 | 3.308754 | 0.5738578 | NA | NA |
| ASV521 | 1.4049190 | 1.5595255 | 3.324962 | 0.4690357 | NA | NA |
| ASV522 | 6.9852153 | 3.3538173 | 3.217753 | 1.0422854 | 0.2972794 | 0.9568046 |
| ASV523 | 0.8317690 | -2.6343256 | 3.406033 | -0.7734292 | 0.4392684 | 0.9919616 |
| ASV524 | 2.4756094 | 1.0398993 | 3.308792 | 0.3142837 | NA | NA |
| ASV525 | 2.3576093 | -4.1442696 | 3.395248 | -1.2206089 | 0.2222341 | 0.8845429 |
| ASV526 | 2.6862027 | -4.3273613 | 3.394550 | -1.2747968 | 0.2023812 | 0.8791834 |
| ASV527 | 5.4731279 | 6.4792147 | 3.325094 | 1.9485809 | NA | NA |
| ASV528 | 1.9096601 | -3.8351181 | 3.396646 | -1.1290897 | 0.2588600 | 0.9179515 |
| ASV529 | 4.7518282 | 1.8717140 | 3.297526 | 0.5676116 | NA | NA |
| ASV530 | 2.6115207 | 2.4179023 | 3.309369 | 0.7306234 | NA | NA |
| ASV531 | 0.7152892 | -2.4219293 | 3.408660 | -0.7105224 | 0.4773802 | 0.9919616 |
| ASV532 | 3.8696715 | 3.2796212 | 3.306362 | 0.9919124 | NA | NA |
| ASV533 | 0.9355717 | 4.2764928 | 3.337169 | 1.2814732 | NA | NA |
| ASV534 | 3.6924976 | 4.2743293 | 3.319008 | 1.2878335 | NA | NA |
| ASV535 | 3.1687303 | 1.3049795 | 3.303241 | 0.3950603 | NA | NA |
| ASV536 | 0.6086405 | -2.2016835 | 3.411820 | -0.6453106 | 0.5187259 | 0.9919616 |
| ASV537 | 5.8014094 | 1.2609867 | 3.295224 | 0.3826710 | 0.7019637 | 0.9919616 |
| ASV538 | 1.9427797 | -0.4474780 | 3.329109 | -0.1344137 | 0.8930754 | 0.9919616 |
| ASV539 | 3.9430191 | -4.8848708 | 3.392896 | -1.4397350 | 0.1499424 | 0.8239591 |
| ASV540 | 1.2851406 | 1.8692532 | 3.329799 | 0.5613712 | NA | NA |
| ASV541 | 4.4770362 | 5.5667952 | 3.325004 | 1.6742220 | 0.0940870 | 0.8011152 |
| ASV542 | 0.6891409 | -2.3415078 | 3.409759 | -0.6867077 | 0.4922669 | 0.9919616 |
| ASV543 | 0.8174920 | -2.6208135 | 3.406189 | -0.7694269 | 0.4416399 | 0.9919616 |
| ASV544 | 3.2208127 | -4.5886563 | 3.393695 | -1.3521121 | 0.1763395 | 0.8239591 |
| ASV545 | 1.1798472 | -3.1420619 | 3.401110 | -0.9238343 | 0.3555726 | 0.9919616 |
| ASV546 | 1.2847054 | 1.3156473 | 3.329223 | 0.3951815 | NA | NA |
| ASV547 | 2.2870624 | 3.1179216 | 3.318576 | 0.9395361 | 0.3474556 | 0.9919616 |
| ASV548 | 1.8426105 | -3.7853417 | 3.396901 | -1.1143517 | 0.2651284 | 0.9179515 |
| ASV549 | 4.1550795 | 2.6360726 | 3.301294 | 0.7984969 | 0.4245822 | 0.9919616 |
| ASV550 | 1.1482260 | 2.2035884 | 3.335215 | 0.6607036 | NA | NA |
| ASV551 | 2.6363579 | -4.3017006 | 3.394642 | -1.2672028 | 0.2050828 | 0.8791834 |
| ASV552 | 0.9606208 | 0.9642660 | 3.346299 | 0.2881590 | 0.7732250 | 0.9919616 |
| ASV553 | 0.4904125 | -1.8835702 | 3.416901 | -0.5512510 | 0.5814617 | 0.9919616 |
| ASV554 | 1.6106202 | 0.5682425 | 3.323866 | 0.1709583 | NA | NA |
| ASV555 | 0.4349425 | 1.8073549 | 3.379080 | 0.5348661 | 0.5927425 | 0.9919616 |
| ASV556 | 0.6202405 | -0.1226365 | 3.397837 | -0.0360925 | 0.9712086 | 0.9919616 |
| ASV557 | 0.8806597 | 2.1304638 | 3.343906 | 0.6371183 | 0.5240478 | 0.9919616 |
#umbral
alpha = 0.05
# Ordenamos la tabla de resultados
res.PR.sCR = res.PR.sCR [order(res.PR.sCR $padj, na.last=NA), ]
# Filtramos según nuestro umbral alpha
sigtab = res.PR.sCR [(res.PR.sCR $padj < alpha), ]
# Le agregamos la taxonomía a la tabla
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(psd5)[rownames(sigtab), ], "matrix"))
# Manipulaciones varias para finalmente graficar los resultados
sigtabgen = subset(sigtab, !is.na(Genus))
# Phylum order
x = tapply(sigtabgen$log2FoldChange, sigtabgen$Phylum, function(x) max(x))
x = sort(x, TRUE)
sigtabgen$Phylum = factor(as.character(sigtabgen$Phylum), levels=names(x))
# Genus order
x = tapply(sigtabgen$log2FoldChange, sigtabgen$Genus, function(x) max(x))
x = sort(x, TRUE)
sigtabgen$Genus = factor(as.character(sigtabgen$Genus), levels=names(x))
ggplot(sigtabgen, aes(y=Genus, x=log2FoldChange, color=Phylum)) +
geom_vline(xintercept = 0.0, color = "gray", size = 0.5) +
geom_point(size=4) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5, size = 10), axis.text.y = element_text(size = 13), legend.text = element_text(size = 13) )
df_res <- cbind(as(res.PR.sCR, "data.frame"), as(tax_table(psd5)[rownames(res.PR.sCR), ], "matrix"))
df_res <- df_res %>%
rownames_to_column(var = "OTU") %>%
arrange(padj)
(fdr_otu <- df_res %>%
dplyr::filter(padj < 0.05))
## OTU baseMean log2FoldChange lfcSE stat pvalue padj
## 1 ASV143 90.57919 -23.647542 2.798983 -8.448620 2.947668e-17 1.143695e-14
## 2 ASV78 138.27535 -24.242780 3.338224 -7.262179 3.809041e-13 3.986267e-11
## 3 ASV105 112.93822 -23.953855 3.303113 -7.251903 4.109554e-13 3.986267e-11
## 4 ASV110 108.77143 -23.903707 3.290335 -7.264825 3.735217e-13 3.986267e-11
## 5 ASV198 50.85653 -22.848421 3.389657 -6.740629 1.577022e-11 1.223769e-09
## 6 ASV334 31.80996 -22.194766 3.351546 -6.622247 3.537789e-11 2.287770e-09
## 7 ASV330 25.72342 -21.897927 3.388500 -6.462424 1.030386e-10 5.711284e-09
## 8 ASV48 21.06044 -21.618912 3.390043 -6.377180 1.803782e-10 7.063576e-09
## 9 ASV61 20.96650 -21.614137 3.390046 -6.375766 1.820509e-10 7.063576e-09
## 10 ASV276 21.57568 -21.653614 3.390027 -6.387446 1.686793e-10 7.063576e-09
## 11 ASV95 18.96324 -21.466058 3.390115 -6.331955 2.420742e-10 8.046069e-09
## 12 ASV173 27.80827 -21.431890 3.389883 -6.322310 2.576820e-10 8.046069e-09
## 13 ASV228 18.09508 -21.409920 3.390150 -6.315330 2.695848e-10 8.046069e-09
## 14 ASV299 14.98841 -21.146695 3.369857 -6.275251 3.490714e-10 9.674264e-09
## 15 ASV128 14.00293 -21.052789 3.390374 -6.209577 5.312756e-10 1.374233e-08
## 16 ASV165 26.28655 -20.722081 3.389912 -6.112868 9.785659e-10 2.373022e-08
## 17 ASV147 76.99358 -9.171426 2.113207 -4.340051 1.424494e-05 3.251197e-04
## 18 ASV171 65.75344 -8.943829 2.569298 -3.481040 4.994711e-04 1.076638e-02
## 19 ASV215 46.91935 -8.456888 2.448534 -3.453857 5.526295e-04 1.128528e-02
## 20 ASV259 35.65692 -8.061065 2.652187 -3.039403 2.370474e-03 4.598719e-02
## Kingdom Phylum Class Order
## 1 Bacteria Actinobacteriota Actinobacteria Bifidobacteriales
## 2 Bacteria Actinobacteriota Actinobacteria Bifidobacteriales
## 3 Bacteria Actinobacteriota Actinobacteria Bifidobacteriales
## 4 Bacteria Actinobacteriota Actinobacteria Bifidobacteriales
## 5 Bacteria Bacteroidota Bacteroidia Bacteroidales
## 6 Bacteria Firmicutes Clostridia Lachnospirales
## 7 Bacteria Actinobacteriota Coriobacteriia Coriobacteriales
## 8 Bacteria Firmicutes Bacilli Lactobacillales
## 9 Bacteria Firmicutes Bacilli Lactobacillales
## 10 Bacteria Firmicutes Clostridia Oscillospirales
## 11 Bacteria Firmicutes Bacilli Lactobacillales
## 12 Bacteria Bacteroidota Bacteroidia Bacteroidales
## 13 Bacteria Bacteroidota Bacteroidia Bacteroidales
## 14 Bacteria Firmicutes Clostridia Oscillospirales
## 15 Bacteria Firmicutes Bacilli Lactobacillales
## 16 Bacteria Bacteroidota Bacteroidia Bacteroidales
## 17 Bacteria Firmicutes Bacilli Lactobacillales
## 18 Bacteria Firmicutes Bacilli Lactobacillales
## 19 Bacteria Firmicutes Bacilli Lactobacillales
## 20 Bacteria Firmicutes Clostridia Lachnospirales
## Family Genus
## 1 Bifidobacteriaceae Bifidobacterium
## 2 Bifidobacteriaceae Bifidobacterium
## 3 Bifidobacteriaceae Bifidobacterium
## 4 Bifidobacteriaceae Bifidobacterium
## 5 Bacteroidaceae Bacteroides
## 6 Lachnospiraceae Coprococcus
## 7 Eggerthellaceae Senegalimassilia
## 8 Lactobacillaceae Ligilactobacillus
## 9 Lactobacillaceae Ligilactobacillus
## 10 Ruminococcaceae Faecalibacterium
## 11 Lactobacillaceae Ligilactobacillus
## 12 Bacteroidaceae Bacteroides
## 13 Bacteroidaceae Bacteroides
## 14 Ruminococcaceae Faecalibacterium
## 15 Lactobacillaceae Ligilactobacillus
## 16 Bacteroidaceae Bacteroides
## 17 Streptococcaceae Streptococcus
## 18 Streptococcaceae Streptococcus
## 19 Streptococcaceae Streptococcus
## 20 Lachnospiraceae [Ruminococcus] torques group
#Metadata counts
as(sample_data(psd5), "data.frame") -> aaa
library(plyr)
counts <- ddply(aaa, .(aaa$Respuesta_100, aaa$Sexo), nrow)
names(counts) <- c("Respuesta_100", "Sexo", "Freq")
counts
## Respuesta_100 Sexo Freq
## 1 PR HOMBRE 4
## 2 PR MUJER 2
## 3 sCR HOMBRE 12
## 4 sCR MUJER 5
list(fdr_otu$OTU)
## [[1]]
## [1] "ASV143" "ASV78" "ASV105" "ASV110" "ASV198" "ASV334" "ASV330" "ASV48"
## [9] "ASV61" "ASV276" "ASV95" "ASV173" "ASV228" "ASV299" "ASV128" "ASV165"
## [17] "ASV147" "ASV171" "ASV215" "ASV259"
#Create a phyloseq object OTUS significatives
GP.chl = prune_taxa(taxa_names(psd5) %in% c("ASV143", "ASV78", "ASV105", "ASV110", "ASV198", "ASV334", "ASV330", "ASV48", "ASV61","ASV276", "ASV95", "ASV173", "ASV228", "ASV299", "ASV128", "ASV165", "ASV147", "ASV171", "ASV215", "ASV259"), psd5)
GP.chl
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 20 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 20 taxa by 6 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 20 tips and 19 internal nodes ]
taxa_sums(GP.chl)
## ASV48 ASV61 ASV78 ASV95 ASV105 ASV110 ASV128 ASV143 ASV147 ASV165 ASV171
## 3673 3338 2556 2609 2089 2027 1890 1698 1729 1885 1575
## ASV173 ASV198 ASV215 ASV228 ASV259 ASV276 ASV299 ASV330 ASV334
## 1849 1483 1072 1176 861 436 355 686 647
#Comparation of abundance
taxa_names(GP.chl)
## [1] "ASV48" "ASV61" "ASV78" "ASV95" "ASV105" "ASV110" "ASV128" "ASV143"
## [9] "ASV147" "ASV165" "ASV171" "ASV173" "ASV198" "ASV215" "ASV228" "ASV259"
## [17] "ASV276" "ASV299" "ASV330" "ASV334"
#metadata, taxonomíaa y abundancia del taxon más abundante de cada muestra
df2 <- psmelt(GP.chl)
head(df2) %>%
kable(format = "html", col.names = colnames(df2)) %>%
kable_styling() %>%
kableExtra::scroll_box(width = "100%", height = "400px")
| OTU | Sample | Abundance | SampleID | Edad_a_la_inclusion | Sexo | Respuesta_100 | aloTPH_previo_si_no | Citogenética_alto_riesgo | Afect_Extramedular_PET_screening_si_no | Dias_producción | CRS_si_no | Grado_máx_CRS | HOSPITAL | Visit | Kingdom | Phylum | Class | Order | Family | Genus | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 379 | ASV48 | HCB12-V2 | 1843 | HCB12-V2 | 64 | HOMBRE | sCR | NO | SI | NO | 110 | NO | NA | HCB | V2 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus |
| 401 | ASV61 | HCB12-V2 | 1778 | HCB12-V2 | 64 | HOMBRE | sCR | NO | SI | NO | 110 | NO | NA | HCB | V2 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus |
| 381 | ASV48 | HVA04-V2 | 1272 | HVA04-V2 | 74 | MUJER | sCR | NO | NO | NO | 100 | SI | 10 | HVA | V2 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus |
| 442 | ASV95 | HCB12-V2 | 1115 | HCB12-V2 | 64 | HOMBRE | sCR | NO | SI | NO | 110 | NO | NA | HCB | V2 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus |
| 134 | ASV165 | ISBS03-V2 | 1061 | ISBS03-V2 | 70 | MUJER | sCR | NO | SI | 100 | SI | 10 | ISBS | V2 | Bacteria | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Bacteroides | |
| 399 | ASV61 | HVA04-V2 | 1029 | HVA04-V2 | 74 | MUJER | sCR | NO | NO | NO | 100 | SI | 10 | HVA | V2 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus |
#Filtrar OTUS sifnificativas
xx <- df %>%
group_by(OTU)%>%
filter(OTU %in% c("ASV143", "ASV78", "ASV105", "ASV110", "ASV198", "ASV334", "ASV330", "ASV48", "ASV61","ASV276", "ASV95", "ASV173", "ASV228", "ASV299", "ASV128", "ASV165", "ASV147", "ASV171", "ASV215", "ASV259"))
xx
## # A tibble: 460 × 21
## # Groups: OTU [20]
## OTU Sample Abundance SampleID Edad_a_la_inclusion Sexo Respuesta_100
## <chr> <chr> <int> <chr> <int> <chr> <fct>
## 1 ASV48 HCB12-V2 1843 HCB12-V2 64 HOMBRE sCR
## 2 ASV61 HCB12-V2 1778 HCB12-V2 64 HOMBRE sCR
## 3 ASV48 HVA04-V2 1272 HVA04-V2 74 MUJER sCR
## 4 ASV95 HCB12-V2 1115 HCB12-V2 64 HOMBRE sCR
## 5 ASV165 ISBS03-V2 1061 ISBS03-V2 70 MUJER sCR
## 6 ASV61 HVA04-V2 1029 HVA04-V2 74 MUJER sCR
## 7 ASV95 HVA04-V2 1020 HVA04-V2 74 MUJER sCR
## 8 ASV173 ISBS03-V2 1003 ISBS03-V2 70 MUJER sCR
## 9 ASV198 ISBS03-V2 819 ISBS03-V2 70 MUJER sCR
## 10 ASV128 HCB12-V2 780 HCB12-V2 64 HOMBRE sCR
## # … with 450 more rows, and 14 more variables: aloTPH_previo_si_no <chr>,
## # Citogenética_alto_riesgo <chr>,
## # Afect_Extramedular_PET_screening_si_no <chr>, Dias_producción <int>,
## # CRS_si_no <chr>, Grado_máx_CRS <int>, HOSPITAL <chr>, Visit <chr>,
## # Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>, Family <chr>,
## # Genus <chr>
#Save table in .csv file
write_csv(xx, file="xx.csv")